Directory structure:
└── modelcontextprotocol-python-sdk/
    └── examples/
        ├── README.md
        ├── clients/
        │   └── simple-chatbot/
        │       ├── README.MD
        │       ├── pyproject.toml
        │       ├── uv.lock
        │       ├── .python-version
        │       └── mcp_simple_chatbot/
        │           ├── main.py
        │           ├── requirements.txt
        │           ├── servers_config.json
        │           ├── test.db
        │           └── .env.example
        ├── fastmcp/
        │   ├── complex_inputs.py
        │   ├── desktop.py
        │   ├── echo.py
        │   ├── memory.py
        │   ├── parameter_descriptions.py
        │   ├── readme-quickstart.py
        │   ├── screenshot.py
        │   ├── simple_echo.py
        │   ├── text_me.py
        │   └── unicode_example.py
        └── servers/
            ├── simple-prompt/
            │   ├── README.md
            │   ├── pyproject.toml
            │   ├── .python-version
            │   └── mcp_simple_prompt/
            │       ├── __init__.py
            │       ├── __main__.py
            │       └── server.py
            ├── simple-resource/
            │   ├── README.md
            │   ├── pyproject.toml
            │   ├── .python-version
            │   └── mcp_simple_resource/
            │       ├── __init__.py
            │       ├── __main__.py
            │       └── server.py
            └── simple-tool/
                ├── README.md
                ├── pyproject.toml
                ├── .python-version
                └── mcp_simple_tool/
                    ├── __init__.py
                    ├── __main__.py
                    └── server.py

================================================
File: examples/README.md
================================================
# Python SDK Examples

This folders aims to provide simple examples of using the Python SDK. Please refer to the
[servers repository](https://github.com/modelcontextprotocol/servers)
for real-world servers.



================================================
File: examples/clients/simple-chatbot/README.MD
================================================
# MCP Simple Chatbot

This example demonstrates how to integrate the Model Context Protocol (MCP) into a simple CLI chatbot. The implementation showcases MCP's flexibility by supporting multiple tools through MCP servers and is compatible with any LLM provider that follows OpenAI API standards.

## Requirements

- Python 3.10
- `python-dotenv`
- `requests`
- `mcp`
- `uvicorn`

## Installation

1. **Install the dependencies:**

   ```bash
   pip install -r requirements.txt
   ```

2. **Set up environment variables:**

   Create a `.env` file in the root directory and add your API key:

   ```plaintext
   LLM_API_KEY=your_api_key_here
   ```

3. **Configure servers:**

   The `servers_config.json` follows the same structure as Claude Desktop, allowing for easy integration of multiple servers. 
   Here's an example:

   ```json
   {
     "mcpServers": {
       "sqlite": {
         "command": "uvx",
         "args": ["mcp-server-sqlite", "--db-path", "./test.db"]
       },
       "puppeteer": {
         "command": "npx",
         "args": ["-y", "@modelcontextprotocol/server-puppeteer"]
       }
     }
   }
   ```
   Environment variables are supported as well. Pass them as you would with the Claude Desktop App.

   Example:
   ```json
   {
     "mcpServers": {
       "server_name": {
         "command": "uvx",
         "args": ["mcp-server-name", "--additional-args"],
         "env": {
           "API_KEY": "your_api_key_here"
         }
       }
     }
   }
   ```

## Usage

1. **Run the client:**

   ```bash
   python main.py
   ```

2. **Interact with the assistant:**
   
   The assistant will automatically detect available tools and can respond to queries based on the tools provided by the configured servers.

3. **Exit the session:**

   Type `quit` or `exit` to end the session.

## Architecture

- **Tool Discovery**: Tools are automatically discovered from configured servers.
- **System Prompt**: Tools are dynamically included in the system prompt, allowing the LLM to understand available capabilities.
- **Server Integration**: Supports any MCP-compatible server, tested with various server implementations including Uvicorn and Node.js.

### Class Structure
- **Configuration**: Manages environment variables and server configurations
- **Server**: Handles MCP server initialization, tool discovery, and execution
- **Tool**: Represents individual tools with their properties and formatting
- **LLMClient**: Manages communication with the LLM provider
- **ChatSession**: Orchestrates the interaction between user, LLM, and tools

### Logic Flow

1. **Tool Integration**:
   - Tools are dynamically discovered from MCP servers
   - Tool descriptions are automatically included in system prompt
   - Tool execution is handled through standardized MCP protocol

2. **Runtime Flow**:
   - User input is received
   - Input is sent to LLM with context of available tools
   - LLM response is parsed:
     - If it's a tool call → execute tool and return result
     - If it's a direct response → return to user
   - Tool results are sent back to LLM for interpretation
   - Final response is presented to user





================================================
File: examples/clients/simple-chatbot/pyproject.toml
================================================
[project]
name = "mcp-simple-chatbot"
version = "0.1.0"
description = "A simple CLI chatbot using the Model Context Protocol (MCP)"
readme = "README.md"
requires-python = ">=3.10"
authors = [{ name = "Edoardo Cilia" }]
keywords = ["mcp", "llm", "chatbot", "cli"]
license = { text = "MIT" }
classifiers = [
    "Development Status :: 4 - Beta",
    "Intended Audience :: Developers",
    "License :: OSI Approved :: MIT License",
    "Programming Language :: Python :: 3",
    "Programming Language :: Python :: 3.10",
]
dependencies = [
    "python-dotenv>=1.0.0",
    "requests>=2.31.0",
    "mcp>=1.0.0",
    "uvicorn>=0.32.1"
]

[project.scripts]
mcp-simple-chatbot = "mcp_simple_chatbot.client:main"

[build-system]
requires = ["hatchling"]
build-backend = "hatchling.build"

[tool.hatch.build.targets.wheel]
packages = ["mcp_simple_chatbot"]

[tool.pyright]
include = ["mcp_simple_chatbot"]
venvPath = "."
venv = ".venv"

[tool.ruff.lint]
select = ["E", "F", "I"]
ignore = []

[tool.ruff]
line-length = 88
target-version = "py310"

[tool.uv]
dev-dependencies = ["pyright>=1.1.379", "pytest>=8.3.3", "ruff>=0.6.9"]



================================================
File: examples/clients/simple-chatbot/uv.lock
================================================
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================================================
File: examples/clients/simple-chatbot/.python-version
================================================
3.10



================================================
File: examples/clients/simple-chatbot/mcp_simple_chatbot/main.py
================================================
import asyncio
import json
import logging
import os
import shutil
from contextlib import AsyncExitStack
from typing import Any

import httpx
from dotenv import load_dotenv
from mcp import ClientSession, StdioServerParameters
from mcp.client.stdio import stdio_client

# Configure logging
logging.basicConfig(
    level=logging.INFO, format="%(asctime)s - %(levelname)s - %(message)s"
)


class Configuration:
    """Manages configuration and environment variables for the MCP client."""

    def __init__(self) -> None:
        """Initialize configuration with environment variables."""
        self.load_env()
        self.api_key = os.getenv("LLM_API_KEY")

    @staticmethod
    def load_env() -> None:
        """Load environment variables from .env file."""
        load_dotenv()

    @staticmethod
    def load_config(file_path: str) -> dict[str, Any]:
        """Load server configuration from JSON file.

        Args:
            file_path: Path to the JSON configuration file.

        Returns:
            Dict containing server configuration.

        Raises:
            FileNotFoundError: If configuration file doesn't exist.
            JSONDecodeError: If configuration file is invalid JSON.
        """
        with open(file_path, "r") as f:
            return json.load(f)

    @property
    def llm_api_key(self) -> str:
        """Get the LLM API key.

        Returns:
            The API key as a string.

        Raises:
            ValueError: If the API key is not found in environment variables.
        """
        if not self.api_key:
            raise ValueError("LLM_API_KEY not found in environment variables")
        return self.api_key


class Server:
    """Manages MCP server connections and tool execution."""

    def __init__(self, name: str, config: dict[str, Any]) -> None:
        self.name: str = name
        self.config: dict[str, Any] = config
        self.stdio_context: Any | None = None
        self.session: ClientSession | None = None
        self._cleanup_lock: asyncio.Lock = asyncio.Lock()
        self.exit_stack: AsyncExitStack = AsyncExitStack()

    async def initialize(self) -> None:
        """Initialize the server connection."""
        command = (
            shutil.which("npx")
            if self.config["command"] == "npx"
            else self.config["command"]
        )
        if command is None:
            raise ValueError("The command must be a valid string and cannot be None.")

        server_params = StdioServerParameters(
            command=command,
            args=self.config["args"],
            env={**os.environ, **self.config["env"]}
            if self.config.get("env")
            else None,
        )
        try:
            stdio_transport = await self.exit_stack.enter_async_context(
                stdio_client(server_params)
            )
            read, write = stdio_transport
            session = await self.exit_stack.enter_async_context(
                ClientSession(read, write)
            )
            await session.initialize()
            self.session = session
        except Exception as e:
            logging.error(f"Error initializing server {self.name}: {e}")
            await self.cleanup()
            raise

    async def list_tools(self) -> list[Any]:
        """List available tools from the server.

        Returns:
            A list of available tools.

        Raises:
            RuntimeError: If the server is not initialized.
        """
        if not self.session:
            raise RuntimeError(f"Server {self.name} not initialized")

        tools_response = await self.session.list_tools()
        tools = []

        for item in tools_response:
            if isinstance(item, tuple) and item[0] == "tools":
                for tool in item[1]:
                    tools.append(Tool(tool.name, tool.description, tool.inputSchema))

        return tools

    async def execute_tool(
        self,
        tool_name: str,
        arguments: dict[str, Any],
        retries: int = 2,
        delay: float = 1.0,
    ) -> Any:
        """Execute a tool with retry mechanism.

        Args:
            tool_name: Name of the tool to execute.
            arguments: Tool arguments.
            retries: Number of retry attempts.
            delay: Delay between retries in seconds.

        Returns:
            Tool execution result.

        Raises:
            RuntimeError: If server is not initialized.
            Exception: If tool execution fails after all retries.
        """
        if not self.session:
            raise RuntimeError(f"Server {self.name} not initialized")

        attempt = 0
        while attempt < retries:
            try:
                logging.info(f"Executing {tool_name}...")
                result = await self.session.call_tool(tool_name, arguments)

                return result

            except Exception as e:
                attempt += 1
                logging.warning(
                    f"Error executing tool: {e}. Attempt {attempt} of {retries}."
                )
                if attempt < retries:
                    logging.info(f"Retrying in {delay} seconds...")
                    await asyncio.sleep(delay)
                else:
                    logging.error("Max retries reached. Failing.")
                    raise

    async def cleanup(self) -> None:
        """Clean up server resources."""
        async with self._cleanup_lock:
            try:
                await self.exit_stack.aclose()
                self.session = None
                self.stdio_context = None
            except Exception as e:
                logging.error(f"Error during cleanup of server {self.name}: {e}")


class Tool:
    """Represents a tool with its properties and formatting."""

    def __init__(
        self, name: str, description: str, input_schema: dict[str, Any]
    ) -> None:
        self.name: str = name
        self.description: str = description
        self.input_schema: dict[str, Any] = input_schema

    def format_for_llm(self) -> str:
        """Format tool information for LLM.

        Returns:
            A formatted string describing the tool.
        """
        args_desc = []
        if "properties" in self.input_schema:
            for param_name, param_info in self.input_schema["properties"].items():
                arg_desc = (
                    f"- {param_name}: {param_info.get('description', 'No description')}"
                )
                if param_name in self.input_schema.get("required", []):
                    arg_desc += " (required)"
                args_desc.append(arg_desc)

        return f"""
Tool: {self.name}
Description: {self.description}
Arguments:
{chr(10).join(args_desc)}
"""


class LLMClient:
    """Manages communication with the LLM provider."""

    def __init__(self, api_key: str) -> None:
        self.api_key: str = api_key

    def get_response(self, messages: list[dict[str, str]]) -> str:
        """Get a response from the LLM.

        Args:
            messages: A list of message dictionaries.

        Returns:
            The LLM's response as a string.

        Raises:
            httpx.RequestError: If the request to the LLM fails.
        """
        url = "https://api.groq.com/openai/v1/chat/completions"

        headers = {
            "Content-Type": "application/json",
            "Authorization": f"Bearer {self.api_key}",
        }
        payload = {
            "messages": messages,
            "model": "llama-3.2-90b-vision-preview",
            "temperature": 0.7,
            "max_tokens": 4096,
            "top_p": 1,
            "stream": False,
            "stop": None,
        }

        try:
            with httpx.Client() as client:
                response = client.post(url, headers=headers, json=payload)
                response.raise_for_status()
                data = response.json()
                return data["choices"][0]["message"]["content"]

        except httpx.RequestError as e:
            error_message = f"Error getting LLM response: {str(e)}"
            logging.error(error_message)

            if isinstance(e, httpx.HTTPStatusError):
                status_code = e.response.status_code
                logging.error(f"Status code: {status_code}")
                logging.error(f"Response details: {e.response.text}")

            return (
                f"I encountered an error: {error_message}. "
                "Please try again or rephrase your request."
            )


class ChatSession:
    """Orchestrates the interaction between user, LLM, and tools."""

    def __init__(self, servers: list[Server], llm_client: LLMClient) -> None:
        self.servers: list[Server] = servers
        self.llm_client: LLMClient = llm_client

    async def cleanup_servers(self) -> None:
        """Clean up all servers properly."""
        cleanup_tasks = []
        for server in self.servers:
            cleanup_tasks.append(asyncio.create_task(server.cleanup()))

        if cleanup_tasks:
            try:
                await asyncio.gather(*cleanup_tasks, return_exceptions=True)
            except Exception as e:
                logging.warning(f"Warning during final cleanup: {e}")

    async def process_llm_response(self, llm_response: str) -> str:
        """Process the LLM response and execute tools if needed.

        Args:
            llm_response: The response from the LLM.

        Returns:
            The result of tool execution or the original response.
        """
        import json

        try:
            tool_call = json.loads(llm_response)
            if "tool" in tool_call and "arguments" in tool_call:
                logging.info(f"Executing tool: {tool_call['tool']}")
                logging.info(f"With arguments: {tool_call['arguments']}")

                for server in self.servers:
                    tools = await server.list_tools()
                    if any(tool.name == tool_call["tool"] for tool in tools):
                        try:
                            result = await server.execute_tool(
                                tool_call["tool"], tool_call["arguments"]
                            )

                            if isinstance(result, dict) and "progress" in result:
                                progress = result["progress"]
                                total = result["total"]
                                percentage = (progress / total) * 100
                                logging.info(
                                    f"Progress: {progress}/{total} "
                                    f"({percentage:.1f}%)"
                                )

                            return f"Tool execution result: {result}"
                        except Exception as e:
                            error_msg = f"Error executing tool: {str(e)}"
                            logging.error(error_msg)
                            return error_msg

                return f"No server found with tool: {tool_call['tool']}"
            return llm_response
        except json.JSONDecodeError:
            return llm_response

    async def start(self) -> None:
        """Main chat session handler."""
        try:
            for server in self.servers:
                try:
                    await server.initialize()
                except Exception as e:
                    logging.error(f"Failed to initialize server: {e}")
                    await self.cleanup_servers()
                    return

            all_tools = []
            for server in self.servers:
                tools = await server.list_tools()
                all_tools.extend(tools)

            tools_description = "\n".join([tool.format_for_llm() for tool in all_tools])

            system_message = (
                "You are a helpful assistant with access to these tools:\n\n"
                f"{tools_description}\n"
                "Choose the appropriate tool based on the user's question. "
                "If no tool is needed, reply directly.\n\n"
                "IMPORTANT: When you need to use a tool, you must ONLY respond with "
                "the exact JSON object format below, nothing else:\n"
                "{\n"
                '    "tool": "tool-name",\n'
                '    "arguments": {\n'
                '        "argument-name": "value"\n'
                "    }\n"
                "}\n\n"
                "After receiving a tool's response:\n"
                "1. Transform the raw data into a natural, conversational response\n"
                "2. Keep responses concise but informative\n"
                "3. Focus on the most relevant information\n"
                "4. Use appropriate context from the user's question\n"
                "5. Avoid simply repeating the raw data\n\n"
                "Please use only the tools that are explicitly defined above."
            )

            messages = [{"role": "system", "content": system_message}]

            while True:
                try:
                    user_input = input("You: ").strip().lower()
                    if user_input in ["quit", "exit"]:
                        logging.info("\nExiting...")
                        break

                    messages.append({"role": "user", "content": user_input})

                    llm_response = self.llm_client.get_response(messages)
                    logging.info("\nAssistant: %s", llm_response)

                    result = await self.process_llm_response(llm_response)

                    if result != llm_response:
                        messages.append({"role": "assistant", "content": llm_response})
                        messages.append({"role": "system", "content": result})

                        final_response = self.llm_client.get_response(messages)
                        logging.info("\nFinal response: %s", final_response)
                        messages.append(
                            {"role": "assistant", "content": final_response}
                        )
                    else:
                        messages.append({"role": "assistant", "content": llm_response})

                except KeyboardInterrupt:
                    logging.info("\nExiting...")
                    break

        finally:
            await self.cleanup_servers()


async def main() -> None:
    """Initialize and run the chat session."""
    config = Configuration()
    server_config = config.load_config("servers_config.json")
    servers = [
        Server(name, srv_config)
        for name, srv_config in server_config["mcpServers"].items()
    ]
    llm_client = LLMClient(config.llm_api_key)
    chat_session = ChatSession(servers, llm_client)
    await chat_session.start()


if __name__ == "__main__":
    asyncio.run(main())



================================================
File: examples/clients/simple-chatbot/mcp_simple_chatbot/requirements.txt
================================================
python-dotenv>=1.0.0
requests>=2.31.0
mcp>=1.0.0
uvicorn>=0.32.1


================================================
File: examples/clients/simple-chatbot/mcp_simple_chatbot/servers_config.json
================================================
{
  "mcpServers": {
    "sqlite": {
      "command": "uvx",
      "args": ["mcp-server-sqlite", "--db-path", "./test.db"]
    },
    "puppeteer": {
      "command": "npx",
      "args": ["-y", "@modelcontextprotocol/server-puppeteer"]
    }
  }
}


================================================
File: examples/clients/simple-chatbot/mcp_simple_chatbot/test.db
================================================
[Non-text file]


================================================
File: examples/clients/simple-chatbot/mcp_simple_chatbot/.env.example
================================================
LLM_API_KEY=gsk_1234567890



================================================
File: examples/fastmcp/complex_inputs.py
================================================
"""
FastMCP Complex inputs Example

Demonstrates validation via pydantic with complex models.
"""

from typing import Annotated

from pydantic import BaseModel, Field

from mcp.server.fastmcp import FastMCP

mcp = FastMCP("Shrimp Tank")


class ShrimpTank(BaseModel):
    class Shrimp(BaseModel):
        name: Annotated[str, Field(max_length=10)]

    shrimp: list[Shrimp]


@mcp.tool()
def name_shrimp(
    tank: ShrimpTank,
    # You can use pydantic Field in function signatures for validation.
    extra_names: Annotated[list[str], Field(max_length=10)],
) -> list[str]:
    """List all shrimp names in the tank"""
    return [shrimp.name for shrimp in tank.shrimp] + extra_names



================================================
File: examples/fastmcp/desktop.py
================================================
"""
FastMCP Desktop Example

A simple example that exposes the desktop directory as a resource.
"""

from pathlib import Path

from mcp.server.fastmcp import FastMCP

# Create server
mcp = FastMCP("Demo")


@mcp.resource("dir://desktop")
def desktop() -> list[str]:
    """List the files in the user's desktop"""
    desktop = Path.home() / "Desktop"
    return [str(f) for f in desktop.iterdir()]


@mcp.tool()
def add(a: int, b: int) -> int:
    """Add two numbers"""
    return a + b



================================================
File: examples/fastmcp/echo.py
================================================
"""
FastMCP Echo Server
"""

from mcp.server.fastmcp import FastMCP

# Create server
mcp = FastMCP("Echo Server")


@mcp.tool()
def echo_tool(text: str) -> str:
    """Echo the input text"""
    return text


@mcp.resource("echo://static")
def echo_resource() -> str:
    return "Echo!"


@mcp.resource("echo://{text}")
def echo_template(text: str) -> str:
    """Echo the input text"""
    return f"Echo: {text}"


@mcp.prompt("echo")
def echo_prompt(text: str) -> str:
    return text



================================================
File: examples/fastmcp/memory.py
================================================
# /// script
# dependencies = ["pydantic-ai-slim[openai]", "asyncpg", "numpy", "pgvector"]
# ///

# uv pip install 'pydantic-ai-slim[openai]' asyncpg numpy pgvector

"""
Recursive memory system inspired by the human brain's clustering of memories.
Uses OpenAI's 'text-embedding-3-small' model and pgvector for efficient
similarity search.
"""

import asyncio
import math
import os
from dataclasses import dataclass
from datetime import datetime, timezone
from pathlib import Path
from typing import Annotated, Self

import asyncpg
import numpy as np
from openai import AsyncOpenAI
from pgvector.asyncpg import register_vector  # Import register_vector
from pydantic import BaseModel, Field
from pydantic_ai import Agent

from mcp.server.fastmcp import FastMCP

MAX_DEPTH = 5
SIMILARITY_THRESHOLD = 0.7
DECAY_FACTOR = 0.99
REINFORCEMENT_FACTOR = 1.1

DEFAULT_LLM_MODEL = "openai:gpt-4o"
DEFAULT_EMBEDDING_MODEL = "text-embedding-3-small"

mcp = FastMCP(
    "memory",
    dependencies=[
        "pydantic-ai-slim[openai]",
        "asyncpg",
        "numpy",
        "pgvector",
    ],
)

DB_DSN = "postgresql://postgres:postgres@localhost:54320/memory_db"
# reset memory with rm ~/.fastmcp/{USER}/memory/*
PROFILE_DIR = (
    Path.home() / ".fastmcp" / os.environ.get("USER", "anon") / "memory"
).resolve()
PROFILE_DIR.mkdir(parents=True, exist_ok=True)


def cosine_similarity(a: list[float], b: list[float]) -> float:
    a_array = np.array(a, dtype=np.float64)
    b_array = np.array(b, dtype=np.float64)
    return np.dot(a_array, b_array) / (
        np.linalg.norm(a_array) * np.linalg.norm(b_array)
    )


async def do_ai[T](
    user_prompt: str,
    system_prompt: str,
    result_type: type[T] | Annotated,
    deps=None,
) -> T:
    agent = Agent(
        DEFAULT_LLM_MODEL,
        system_prompt=system_prompt,
        result_type=result_type,
    )
    result = await agent.run(user_prompt, deps=deps)
    return result.data


@dataclass
class Deps:
    openai: AsyncOpenAI
    pool: asyncpg.Pool


async def get_db_pool() -> asyncpg.Pool:
    async def init(conn):
        await conn.execute("CREATE EXTENSION IF NOT EXISTS vector;")
        await register_vector(conn)

    pool = await asyncpg.create_pool(DB_DSN, init=init)
    return pool


class MemoryNode(BaseModel):
    id: int | None = None
    content: str
    summary: str = ""
    importance: float = 1.0
    access_count: int = 0
    timestamp: float = Field(
        default_factory=lambda: datetime.now(timezone.utc).timestamp()
    )
    embedding: list[float]

    @classmethod
    async def from_content(cls, content: str, deps: Deps):
        embedding = await get_embedding(content, deps)
        return cls(content=content, embedding=embedding)

    async def save(self, deps: Deps):
        async with deps.pool.acquire() as conn:
            if self.id is None:
                result = await conn.fetchrow(
                    """
                    INSERT INTO memories (content, summary, importance, access_count,
                        timestamp, embedding)
                    VALUES ($1, $2, $3, $4, $5, $6)
                    RETURNING id
                    """,
                    self.content,
                    self.summary,
                    self.importance,
                    self.access_count,
                    self.timestamp,
                    self.embedding,
                )
                self.id = result["id"]
            else:
                await conn.execute(
                    """
                    UPDATE memories
                    SET content = $1, summary = $2, importance = $3,
                        access_count = $4, timestamp = $5, embedding = $6
                    WHERE id = $7
                    """,
                    self.content,
                    self.summary,
                    self.importance,
                    self.access_count,
                    self.timestamp,
                    self.embedding,
                    self.id,
                )

    async def merge_with(self, other: Self, deps: Deps):
        self.content = await do_ai(
            f"{self.content}\n\n{other.content}",
            "Combine the following two texts into a single, coherent text.",
            str,
            deps,
        )
        self.importance += other.importance
        self.access_count += other.access_count
        self.embedding = [(a + b) / 2 for a, b in zip(self.embedding, other.embedding)]
        self.summary = await do_ai(
            self.content, "Summarize the following text concisely.", str, deps
        )
        await self.save(deps)
        # Delete the merged node from the database
        if other.id is not None:
            await delete_memory(other.id, deps)

    def get_effective_importance(self):
        return self.importance * (1 + math.log(self.access_count + 1))


async def get_embedding(text: str, deps: Deps) -> list[float]:
    embedding_response = await deps.openai.embeddings.create(
        input=text,
        model=DEFAULT_EMBEDDING_MODEL,
    )
    return embedding_response.data[0].embedding


async def delete_memory(memory_id: int, deps: Deps):
    async with deps.pool.acquire() as conn:
        await conn.execute("DELETE FROM memories WHERE id = $1", memory_id)


async def add_memory(content: str, deps: Deps):
    new_memory = await MemoryNode.from_content(content, deps)
    await new_memory.save(deps)

    similar_memories = await find_similar_memories(new_memory.embedding, deps)
    for memory in similar_memories:
        if memory.id != new_memory.id:
            await new_memory.merge_with(memory, deps)

    await update_importance(new_memory.embedding, deps)

    await prune_memories(deps)

    return f"Remembered: {content}"


async def find_similar_memories(embedding: list[float], deps: Deps) -> list[MemoryNode]:
    async with deps.pool.acquire() as conn:
        rows = await conn.fetch(
            """
            SELECT id, content, summary, importance, access_count, timestamp, embedding
            FROM memories
            ORDER BY embedding <-> $1
            LIMIT 5
            """,
            embedding,
        )
    memories = [
        MemoryNode(
            id=row["id"],
            content=row["content"],
            summary=row["summary"],
            importance=row["importance"],
            access_count=row["access_count"],
            timestamp=row["timestamp"],
            embedding=row["embedding"],
        )
        for row in rows
    ]
    return memories


async def update_importance(user_embedding: list[float], deps: Deps):
    async with deps.pool.acquire() as conn:
        rows = await conn.fetch(
            "SELECT id, importance, access_count, embedding FROM memories"
        )
        for row in rows:
            memory_embedding = row["embedding"]
            similarity = cosine_similarity(user_embedding, memory_embedding)
            if similarity > SIMILARITY_THRESHOLD:
                new_importance = row["importance"] * REINFORCEMENT_FACTOR
                new_access_count = row["access_count"] + 1
            else:
                new_importance = row["importance"] * DECAY_FACTOR
                new_access_count = row["access_count"]
            await conn.execute(
                """
                UPDATE memories
                SET importance = $1, access_count = $2
                WHERE id = $3
                """,
                new_importance,
                new_access_count,
                row["id"],
            )


async def prune_memories(deps: Deps):
    async with deps.pool.acquire() as conn:
        rows = await conn.fetch(
            """
            SELECT id, importance, access_count
            FROM memories
            ORDER BY importance DESC
            OFFSET $1
            """,
            MAX_DEPTH,
        )
        for row in rows:
            await conn.execute("DELETE FROM memories WHERE id = $1", row["id"])


async def display_memory_tree(deps: Deps) -> str:
    async with deps.pool.acquire() as conn:
        rows = await conn.fetch(
            """
            SELECT content, summary, importance, access_count
            FROM memories
            ORDER BY importance DESC
            LIMIT $1
            """,
            MAX_DEPTH,
        )
    result = ""
    for row in rows:
        effective_importance = row["importance"] * (
            1 + math.log(row["access_count"] + 1)
        )
        summary = row["summary"] or row["content"]
        result += f"- {summary} (Importance: {effective_importance:.2f})\n"
    return result


@mcp.tool()
async def remember(
    contents: list[str] = Field(
        description="List of observations or memories to store"
    ),
):
    deps = Deps(openai=AsyncOpenAI(), pool=await get_db_pool())
    try:
        return "\n".join(
            await asyncio.gather(*[add_memory(content, deps) for content in contents])
        )
    finally:
        await deps.pool.close()


@mcp.tool()
async def read_profile() -> str:
    deps = Deps(openai=AsyncOpenAI(), pool=await get_db_pool())
    profile = await display_memory_tree(deps)
    await deps.pool.close()
    return profile


async def initialize_database():
    pool = await asyncpg.create_pool(
        "postgresql://postgres:postgres@localhost:54320/postgres"
    )
    try:
        async with pool.acquire() as conn:
            await conn.execute("""
                SELECT pg_terminate_backend(pg_stat_activity.pid)
                FROM pg_stat_activity
                WHERE pg_stat_activity.datname = 'memory_db'
                AND pid <> pg_backend_pid();
            """)
            await conn.execute("DROP DATABASE IF EXISTS memory_db;")
            await conn.execute("CREATE DATABASE memory_db;")
    finally:
        await pool.close()

    pool = await asyncpg.create_pool(DB_DSN)
    try:
        async with pool.acquire() as conn:
            await conn.execute("CREATE EXTENSION IF NOT EXISTS vector;")

            await register_vector(conn)

            await conn.execute("""
                CREATE TABLE IF NOT EXISTS memories (
                    id SERIAL PRIMARY KEY,
                    content TEXT NOT NULL,
                    summary TEXT,
                    importance REAL NOT NULL,
                    access_count INT NOT NULL,
                    timestamp DOUBLE PRECISION NOT NULL,
                    embedding vector(1536) NOT NULL
                );
                CREATE INDEX IF NOT EXISTS idx_memories_embedding ON memories
                    USING hnsw (embedding vector_l2_ops);
            """)
    finally:
        await pool.close()


if __name__ == "__main__":
    asyncio.run(initialize_database())



================================================
File: examples/fastmcp/parameter_descriptions.py
================================================
"""
FastMCP Example showing parameter descriptions
"""

from pydantic import Field

from mcp.server.fastmcp import FastMCP

# Create server
mcp = FastMCP("Parameter Descriptions Server")


@mcp.tool()
def greet_user(
    name: str = Field(description="The name of the person to greet"),
    title: str = Field(description="Optional title like Mr/Ms/Dr", default=""),
    times: int = Field(description="Number of times to repeat the greeting", default=1),
) -> str:
    """Greet a user with optional title and repetition"""
    greeting = f"Hello {title + ' ' if title else ''}{name}!"
    return "\n".join([greeting] * times)



================================================
File: examples/fastmcp/readme-quickstart.py
================================================
from mcp.server.fastmcp import FastMCP

# Create an MCP server
mcp = FastMCP("Demo")


# Add an addition tool
@mcp.tool()
def add(a: int, b: int) -> int:
    """Add two numbers"""
    return a + b


# Add a dynamic greeting resource
@mcp.resource("greeting://{name}")
def get_greeting(name: str) -> str:
    """Get a personalized greeting"""
    return f"Hello, {name}!"



================================================
File: examples/fastmcp/screenshot.py
================================================
"""
FastMCP Screenshot Example

Give Claude a tool to capture and view screenshots.
"""

import io

from mcp.server.fastmcp import FastMCP
from mcp.server.fastmcp.utilities.types import Image

# Create server
mcp = FastMCP("Screenshot Demo", dependencies=["pyautogui", "Pillow"])


@mcp.tool()
def take_screenshot() -> Image:
    """
    Take a screenshot of the user's screen and return it as an image. Use
    this tool anytime the user wants you to look at something they're doing.
    """
    import pyautogui

    buffer = io.BytesIO()

    # if the file exceeds ~1MB, it will be rejected by Claude
    screenshot = pyautogui.screenshot()
    screenshot.convert("RGB").save(buffer, format="JPEG", quality=60, optimize=True)
    return Image(data=buffer.getvalue(), format="jpeg")



================================================
File: examples/fastmcp/simple_echo.py
================================================
"""
FastMCP Echo Server
"""

from mcp.server.fastmcp import FastMCP

# Create server
mcp = FastMCP("Echo Server")


@mcp.tool()
def echo(text: str) -> str:
    """Echo the input text"""
    return text



================================================
File: examples/fastmcp/text_me.py
================================================
# /// script
# dependencies = []
# ///

"""
FastMCP Text Me Server
--------------------------------
This defines a simple FastMCP server that sends a text message to a phone number via https://surgemsg.com/.

To run this example, create a `.env` file with the following values:

SURGE_API_KEY=...
SURGE_ACCOUNT_ID=...
SURGE_MY_PHONE_NUMBER=...
SURGE_MY_FIRST_NAME=...
SURGE_MY_LAST_NAME=...

Visit https://surgemsg.com/ and click "Get Started" to obtain these values.
"""

from typing import Annotated

import httpx
from pydantic import BeforeValidator
from pydantic_settings import BaseSettings, SettingsConfigDict

from mcp.server.fastmcp import FastMCP


class SurgeSettings(BaseSettings):
    model_config: SettingsConfigDict = SettingsConfigDict(
        env_prefix="SURGE_", env_file=".env"
    )

    api_key: str
    account_id: str
    my_phone_number: Annotated[
        str, BeforeValidator(lambda v: "+" + v if not v.startswith("+") else v)
    ]
    my_first_name: str
    my_last_name: str


# Create server
mcp = FastMCP("Text me")
surge_settings = SurgeSettings()  # type: ignore


@mcp.tool(name="textme", description="Send a text message to me")
def text_me(text_content: str) -> str:
    """Send a text message to a phone number via https://surgemsg.com/"""
    with httpx.Client() as client:
        response = client.post(
            "https://api.surgemsg.com/messages",
            headers={
                "Authorization": f"Bearer {surge_settings.api_key}",
                "Surge-Account": surge_settings.account_id,
                "Content-Type": "application/json",
            },
            json={
                "body": text_content,
                "conversation": {
                    "contact": {
                        "first_name": surge_settings.my_first_name,
                        "last_name": surge_settings.my_last_name,
                        "phone_number": surge_settings.my_phone_number,
                    }
                },
            },
        )
        response.raise_for_status()
        return f"Message sent: {text_content}"



================================================
File: examples/fastmcp/unicode_example.py
================================================
"""
Example FastMCP server that uses Unicode characters in various places to help test
Unicode handling in tools and inspectors.
"""

from mcp.server.fastmcp import FastMCP

mcp = FastMCP()


@mcp.tool(
    description="🌟 A tool that uses various Unicode characters in its description: "
    "á é í ó ú ñ 漢字 🎉"
)
def hello_unicode(name: str = "世界", greeting: str = "¡Hola") -> str:
    """
    A simple tool that demonstrates Unicode handling in:
    - Tool description (emojis, accents, CJK characters)
    - Parameter defaults (CJK characters)
    - Return values (Spanish punctuation, emojis)
    """
    return f"{greeting}, {name}! 👋"


@mcp.tool(description="🎨 Tool that returns a list of emoji categories")
def list_emoji_categories() -> list[str]:
    """Returns a list of emoji categories with emoji examples."""
    return [
        "😀 Smileys & Emotion",
        "👋 People & Body",
        "🐶 Animals & Nature",
        "🍎 Food & Drink",
        "⚽ Activities",
        "🌍 Travel & Places",
        "💡 Objects",
        "❤️ Symbols",
        "🚩 Flags",
    ]


@mcp.tool(description="🔤 Tool that returns text in different scripts")
def multilingual_hello() -> str:
    """Returns hello in different scripts and writing systems."""
    return "\n".join(
        [
            "English: Hello!",
            "Spanish: ¡Hola!",
            "French: Bonjour!",
            "German: Grüß Gott!",
            "Russian: Привет!",
            "Greek: Γεια σας!",
            "Hebrew: !שָׁלוֹם",
            "Arabic: !مرحبا",
            "Hindi: नमस्ते!",
            "Chinese: 你好!",
            "Japanese: こんにちは!",
            "Korean: 안녕하세요!",
            "Thai: สวัสดี!",
        ]
    )


if __name__ == "__main__":
    mcp.run()



================================================
File: examples/servers/simple-prompt/README.md
================================================
# MCP Simple Prompt

A simple MCP server that exposes a customizable prompt template with optional context and topic parameters.

## Usage

Start the server using either stdio (default) or SSE transport:

```bash
# Using stdio transport (default)
uv run mcp-simple-prompt

# Using SSE transport on custom port
uv run mcp-simple-prompt --transport sse --port 8000
```

The server exposes a prompt named "simple" that accepts two optional arguments:

- `context`: Additional context to consider
- `topic`: Specific topic to focus on

## Example

Using the MCP client, you can retrieve the prompt like this using the STDIO transport:

```python
import asyncio
from mcp.client.session import ClientSession
from mcp.client.stdio import StdioServerParameters, stdio_client


async def main():
    async with stdio_client(
        StdioServerParameters(command="uv", args=["run", "mcp-simple-prompt"])
    ) as (read, write):
        async with ClientSession(read, write) as session:
            await session.initialize()

            # List available prompts
            prompts = await session.list_prompts()
            print(prompts)

            # Get the prompt with arguments
            prompt = await session.get_prompt(
                "simple",
                {
                    "context": "User is a software developer",
                    "topic": "Python async programming",
                },
            )
            print(prompt)


asyncio.run(main())
```



================================================
File: examples/servers/simple-prompt/pyproject.toml
================================================
[project]
name = "mcp-simple-prompt"
version = "0.1.0"
description = "A simple MCP server exposing a customizable prompt"
readme = "README.md"
requires-python = ">=3.10"
authors = [{ name = "Anthropic, PBC." }]
maintainers = [
    { name = "David Soria Parra", email = "davidsp@anthropic.com" },
    { name = "Justin Spahr-Summers", email = "justin@anthropic.com" },
]
keywords = ["mcp", "llm", "automation", "web", "fetch"]
license = { text = "MIT" }
classifiers = [
    "Development Status :: 4 - Beta",
    "Intended Audience :: Developers",
    "License :: OSI Approved :: MIT License",
    "Programming Language :: Python :: 3",
    "Programming Language :: Python :: 3.10",
]
dependencies = ["anyio>=4.5", "click>=8.1.0", "httpx>=0.27", "mcp"]

[project.scripts]
mcp-simple-prompt = "mcp_simple_prompt.server:main"

[build-system]
requires = ["hatchling"]
build-backend = "hatchling.build"

[tool.hatch.build.targets.wheel]
packages = ["mcp_simple_prompt"]

[tool.pyright]
include = ["mcp_simple_prompt"]
venvPath = "."
venv = ".venv"

[tool.ruff.lint]
select = ["E", "F", "I"]
ignore = []

[tool.ruff]
line-length = 88
target-version = "py310"

[tool.uv]
dev-dependencies = ["pyright>=1.1.378", "pytest>=8.3.3", "ruff>=0.6.9"]



================================================
File: examples/servers/simple-prompt/.python-version
================================================
3.10



================================================
File: examples/servers/simple-prompt/mcp_simple_prompt/__init__.py
================================================




================================================
File: examples/servers/simple-prompt/mcp_simple_prompt/__main__.py
================================================
import sys

from .server import main

sys.exit(main())



================================================
File: examples/servers/simple-prompt/mcp_simple_prompt/server.py
================================================
import anyio
import click
import mcp.types as types
from mcp.server.lowlevel import Server


def create_messages(
    context: str | None = None, topic: str | None = None
) -> list[types.PromptMessage]:
    """Create the messages for the prompt."""
    messages = []

    # Add context if provided
    if context:
        messages.append(
            types.PromptMessage(
                role="user",
                content=types.TextContent(
                    type="text", text=f"Here is some relevant context: {context}"
                ),
            )
        )

    # Add the main prompt
    prompt = "Please help me with "
    if topic:
        prompt += f"the following topic: {topic}"
    else:
        prompt += "whatever questions I may have."

    messages.append(
        types.PromptMessage(
            role="user", content=types.TextContent(type="text", text=prompt)
        )
    )

    return messages


@click.command()
@click.option("--port", default=8000, help="Port to listen on for SSE")
@click.option(
    "--transport",
    type=click.Choice(["stdio", "sse"]),
    default="stdio",
    help="Transport type",
)
def main(port: int, transport: str) -> int:
    app = Server("mcp-simple-prompt")

    @app.list_prompts()
    async def list_prompts() -> list[types.Prompt]:
        return [
            types.Prompt(
                name="simple",
                description="A simple prompt that can take optional context and topic "
                "arguments",
                arguments=[
                    types.PromptArgument(
                        name="context",
                        description="Additional context to consider",
                        required=False,
                    ),
                    types.PromptArgument(
                        name="topic",
                        description="Specific topic to focus on",
                        required=False,
                    ),
                ],
            )
        ]

    @app.get_prompt()
    async def get_prompt(
        name: str, arguments: dict[str, str] | None = None
    ) -> types.GetPromptResult:
        if name != "simple":
            raise ValueError(f"Unknown prompt: {name}")

        if arguments is None:
            arguments = {}

        return types.GetPromptResult(
            messages=create_messages(
                context=arguments.get("context"), topic=arguments.get("topic")
            ),
            description="A simple prompt with optional context and topic arguments",
        )

    if transport == "sse":
        from mcp.server.sse import SseServerTransport
        from starlette.applications import Starlette
        from starlette.routing import Mount, Route

        sse = SseServerTransport("/messages/")

        async def handle_sse(request):
            async with sse.connect_sse(
                request.scope, request.receive, request._send
            ) as streams:
                await app.run(
                    streams[0], streams[1], app.create_initialization_options()
                )

        starlette_app = Starlette(
            debug=True,
            routes=[
                Route("/sse", endpoint=handle_sse),
                Mount("/messages/", app=sse.handle_post_message),
            ],
        )

        import uvicorn

        uvicorn.run(starlette_app, host="0.0.0.0", port=port)
    else:
        from mcp.server.stdio import stdio_server

        async def arun():
            async with stdio_server() as streams:
                await app.run(
                    streams[0], streams[1], app.create_initialization_options()
                )

        anyio.run(arun)

    return 0



================================================
File: examples/servers/simple-resource/README.md
================================================
# MCP Simple Resource

A simple MCP server that exposes sample text files as resources.

## Usage

Start the server using either stdio (default) or SSE transport:

```bash
# Using stdio transport (default)
uv run mcp-simple-resource

# Using SSE transport on custom port
uv run mcp-simple-resource --transport sse --port 8000
```

The server exposes some basic text file resources that can be read by clients.

## Example

Using the MCP client, you can retrieve resources like this using the STDIO transport:

```python
import asyncio
from mcp.types import AnyUrl
from mcp.client.session import ClientSession
from mcp.client.stdio import StdioServerParameters, stdio_client


async def main():
    async with stdio_client(
        StdioServerParameters(command="uv", args=["run", "mcp-simple-resource"])
    ) as (read, write):
        async with ClientSession(read, write) as session:
            await session.initialize()

            # List available resources
            resources = await session.list_resources()
            print(resources)

            # Get a specific resource
            resource = await session.read_resource(AnyUrl("file:///greeting.txt"))
            print(resource)


asyncio.run(main())

```



================================================
File: examples/servers/simple-resource/pyproject.toml
================================================
[project]
name = "mcp-simple-resource"
version = "0.1.0"
description = "A simple MCP server exposing sample text resources"
readme = "README.md"
requires-python = ">=3.10"
authors = [{ name = "Anthropic, PBC." }]
maintainers = [
    { name = "David Soria Parra", email = "davidsp@anthropic.com" },
    { name = "Justin Spahr-Summers", email = "justin@anthropic.com" },
]
keywords = ["mcp", "llm", "automation", "web", "fetch"]
license = { text = "MIT" }
classifiers = [
    "Development Status :: 4 - Beta",
    "Intended Audience :: Developers",
    "License :: OSI Approved :: MIT License",
    "Programming Language :: Python :: 3",
    "Programming Language :: Python :: 3.10",
]
dependencies = ["anyio>=4.5", "click>=8.1.0", "httpx>=0.27", "mcp"]

[project.scripts]
mcp-simple-resource = "mcp_simple_resource.server:main"

[build-system]
requires = ["hatchling"]
build-backend = "hatchling.build"

[tool.hatch.build.targets.wheel]
packages = ["mcp_simple_resource"]

[tool.pyright]
include = ["mcp_simple_resource"]
venvPath = "."
venv = ".venv"

[tool.ruff.lint]
select = ["E", "F", "I"]
ignore = []

[tool.ruff]
line-length = 88
target-version = "py310"

[tool.uv]
dev-dependencies = ["pyright>=1.1.378", "pytest>=8.3.3", "ruff>=0.6.9"]



================================================
File: examples/servers/simple-resource/.python-version
================================================
3.10



================================================
File: examples/servers/simple-resource/mcp_simple_resource/__init__.py
================================================




================================================
File: examples/servers/simple-resource/mcp_simple_resource/__main__.py
================================================
import sys

from server import main

sys.exit(main())



================================================
File: examples/servers/simple-resource/mcp_simple_resource/server.py
================================================
import anyio
import click
import mcp.types as types
from mcp.server.lowlevel import Server
from pydantic import FileUrl

SAMPLE_RESOURCES = {
    "greeting": "Hello! This is a sample text resource.",
    "help": "This server provides a few sample text resources for testing.",
    "about": "This is the simple-resource MCP server implementation.",
}


@click.command()
@click.option("--port", default=8000, help="Port to listen on for SSE")
@click.option(
    "--transport",
    type=click.Choice(["stdio", "sse"]),
    default="stdio",
    help="Transport type",
)
def main(port: int, transport: str) -> int:
    app = Server("mcp-simple-resource")

    @app.list_resources()
    async def list_resources() -> list[types.Resource]:
        return [
            types.Resource(
                uri=FileUrl(f"file:///{name}.txt"),
                name=name,
                description=f"A sample text resource named {name}",
                mimeType="text/plain",
            )
            for name in SAMPLE_RESOURCES.keys()
        ]

    @app.read_resource()
    async def read_resource(uri: FileUrl) -> str | bytes:
        name = uri.path.replace(".txt", "").lstrip("/")

        if name not in SAMPLE_RESOURCES:
            raise ValueError(f"Unknown resource: {uri}")

        return SAMPLE_RESOURCES[name]

    if transport == "sse":
        from mcp.server.sse import SseServerTransport
        from starlette.applications import Starlette
        from starlette.routing import Mount, Route

        sse = SseServerTransport("/messages/")

        async def handle_sse(request):
            async with sse.connect_sse(
                request.scope, request.receive, request._send
            ) as streams:
                await app.run(
                    streams[0], streams[1], app.create_initialization_options()
                )

        starlette_app = Starlette(
            debug=True,
            routes=[
                Route("/sse", endpoint=handle_sse),
                Mount("/messages/", app=sse.handle_post_message),
            ],
        )

        import uvicorn

        uvicorn.run(starlette_app, host="0.0.0.0", port=port)
    else:
        from mcp.server.stdio import stdio_server

        async def arun():
            async with stdio_server() as streams:
                await app.run(
                    streams[0], streams[1], app.create_initialization_options()
                )

        anyio.run(arun)

    return 0



================================================
File: examples/servers/simple-tool/README.md
================================================

A simple MCP server that exposes a website fetching tool.

## Usage

Start the server using either stdio (default) or SSE transport:

```bash
# Using stdio transport (default)
uv run mcp-simple-tool

# Using SSE transport on custom port
uv run mcp-simple-tool --transport sse --port 8000
```

The server exposes a tool named "fetch" that accepts one required argument:

- `url`: The URL of the website to fetch

## Example

Using the MCP client, you can use the tool like this using the STDIO transport:

```python
import asyncio
from mcp.client.session import ClientSession
from mcp.client.stdio import StdioServerParameters, stdio_client


async def main():
    async with stdio_client(
        StdioServerParameters(command="uv", args=["run", "mcp-simple-tool"])
    ) as (read, write):
        async with ClientSession(read, write) as session:
            await session.initialize()

            # List available tools
            tools = await session.list_tools()
            print(tools)

            # Call the fetch tool
            result = await session.call_tool("fetch", {"url": "https://example.com"})
            print(result)


asyncio.run(main())

```



================================================
File: examples/servers/simple-tool/pyproject.toml
================================================
[project]
name = "mcp-simple-tool"
version = "0.1.0"
description = "A simple MCP server exposing a website fetching tool"
readme = "README.md"
requires-python = ">=3.10"
authors = [{ name = "Anthropic, PBC." }]
maintainers = [
    { name = "David Soria Parra", email = "davidsp@anthropic.com" },
    { name = "Justin Spahr-Summers", email = "justin@anthropic.com" },
]
keywords = ["mcp", "llm", "automation", "web", "fetch"]
license = { text = "MIT" }
classifiers = [
    "Development Status :: 4 - Beta",
    "Intended Audience :: Developers",
    "License :: OSI Approved :: MIT License",
    "Programming Language :: Python :: 3",
    "Programming Language :: Python :: 3.10",
]
dependencies = ["anyio>=4.5", "click>=8.1.0", "httpx>=0.27", "mcp"]

[project.scripts]
mcp-simple-tool = "mcp_simple_tool.server:main"

[build-system]
requires = ["hatchling"]
build-backend = "hatchling.build"

[tool.hatch.build.targets.wheel]
packages = ["mcp_simple_tool"]

[tool.pyright]
include = ["mcp_simple_tool"]
venvPath = "."
venv = ".venv"

[tool.ruff.lint]
select = ["E", "F", "I"]
ignore = []

[tool.ruff]
line-length = 88
target-version = "py310"

[tool.uv]
dev-dependencies = ["pyright>=1.1.378", "pytest>=8.3.3", "ruff>=0.6.9"]



================================================
File: examples/servers/simple-tool/.python-version
================================================
3.10



================================================
File: examples/servers/simple-tool/mcp_simple_tool/__init__.py
================================================




================================================
File: examples/servers/simple-tool/mcp_simple_tool/__main__.py
================================================
import sys

from server import main

sys.exit(main())



================================================
File: examples/servers/simple-tool/mcp_simple_tool/server.py
================================================
import anyio
import click
import httpx
import mcp.types as types
from mcp.server.lowlevel import Server


async def fetch_website(
    url: str,
) -> list[types.TextContent | types.ImageContent | types.EmbeddedResource]:
    headers = {
        "User-Agent": "MCP Test Server (github.com/modelcontextprotocol/python-sdk)"
    }
    async with httpx.AsyncClient(follow_redirects=True, headers=headers) as client:
        response = await client.get(url)
        response.raise_for_status()
        return [types.TextContent(type="text", text=response.text)]


@click.command()
@click.option("--port", default=8000, help="Port to listen on for SSE")
@click.option(
    "--transport",
    type=click.Choice(["stdio", "sse"]),
    default="stdio",
    help="Transport type",
)
def main(port: int, transport: str) -> int:
    app = Server("mcp-website-fetcher")

    @app.call_tool()
    async def fetch_tool(
        name: str, arguments: dict
    ) -> list[types.TextContent | types.ImageContent | types.EmbeddedResource]:
        if name != "fetch":
            raise ValueError(f"Unknown tool: {name}")
        if "url" not in arguments:
            raise ValueError("Missing required argument 'url'")
        return await fetch_website(arguments["url"])

    @app.list_tools()
    async def list_tools() -> list[types.Tool]:
        return [
            types.Tool(
                name="fetch",
                description="Fetches a website and returns its content",
                inputSchema={
                    "type": "object",
                    "required": ["url"],
                    "properties": {
                        "url": {
                            "type": "string",
                            "description": "URL to fetch",
                        }
                    },
                },
            )
        ]

    if transport == "sse":
        from mcp.server.sse import SseServerTransport
        from starlette.applications import Starlette
        from starlette.routing import Mount, Route

        sse = SseServerTransport("/messages/")

        async def handle_sse(request):
            async with sse.connect_sse(
                request.scope, request.receive, request._send
            ) as streams:
                await app.run(
                    streams[0], streams[1], app.create_initialization_options()
                )

        starlette_app = Starlette(
            debug=True,
            routes=[
                Route("/sse", endpoint=handle_sse),
                Mount("/messages/", app=sse.handle_post_message),
            ],
        )

        import uvicorn

        uvicorn.run(starlette_app, host="0.0.0.0", port=port)
    else:
        from mcp.server.stdio import stdio_server

        async def arun():
            async with stdio_server() as streams:
                await app.run(
                    streams[0], streams[1], app.create_initialization_options()
                )

        anyio.run(arun)

    return 0


