import logging
from fastmcp import FastMCP
from typing import Dict, Any, List

# Setup detailed logging
logger = logging.getLogger("specfront_ai.mcp_server")
logger.setLevel(logging.INFO)

# Initialize FastMCP Server
mcp = FastMCP("specfront_ai")

logger.info("Initializing specfront_ai FastMCP server tools...")

@mcp.tool()
def get_jira_issue(issue_key: str) -> Dict[str, Any]:
    """
    Fetch details of a specific Jira issue.
    
    Args:
        issue_key: The Jira issue identifier (e.g., 'PROJ-123')
    """
    logger.info(f"Tool called: get_jira_issue for key {issue_key}")
    # Placeholder for actual Jira API integration
    # Typically: requests.get(f"{JIRA_URL}/rest/api/2/issue/{issue_key}", auth=(user, token))
    logger.debug(f"Simulating API call to Jira for {issue_key}...")
    
    return {
        "key": issue_key,
        "summary": "Implement Hotel Inventory models",
        "description": "Create Django models for RoomType, Room, and Amenity as per standard specs.",
        "status": "In Progress"
    }

@mcp.tool()
def create_jira_issue(summary: str, description: str, project_key: str) -> str:
    """
    Create a new Jira issue.
    
    Args:
        summary: Issue title
        description: Detailed description of the issue
        project_key: Key of the Jira project
    """
    logger.info(f"Tool called: create_jira_issue in project {project_key}. Summary: {summary}")
    # Placeholder for actual Jira API integration
    logger.debug("Simulating ticket creation...")
    new_issue_key = f"{project_key}-101"
    logger.info(f"Successfully created simulated Jira issue: {new_issue_key}")
    return new_issue_key

@mcp.tool()
def get_figma_node(file_key: str, node_id: str) -> Dict[str, Any]:
    """
    Retrieve design element details from a Figma file.
    
    Args:
        file_key: The unique identifier of the Figma file
        node_id: The specific node/element ID to inspect
    """
    logger.info(f"Tool called: get_figma_node for file {file_key}, node {node_id}")
    # Placeholder for actual Figma REST API integration
    # Typically: headers = {"X-Figma-Token": token}; requests.get(..., headers=headers)
    logger.debug(f"Fetching Figma node {node_id} details...")
    
    return {
        "id": node_id,
        "name": "Inventory Dashboard Layout",
        "type": "FRAME",
        "properties": {
            "backgroundColor": "#FFFFFF",
            "layoutMode": "VERTICAL",
            "padding": 24
        }
    }

@mcp.tool()
def get_figma_comments(file_key: str) -> List[Dict[str, Any]]:
    """
    Get all comments and feedback left on a specific Figma design file.
    
    Args:
        file_key: The unique identifier of the Figma file
    """
    logger.info(f"Tool called: get_figma_comments for file {file_key}")
    logger.debug("Fetching comments from Figma API...")
    
    return [
        {
            "id": "comment_1",
            "message": "Make sure the dashboard matches the new branding guidelines.",
            "user": "Design Lead",
            "resolved": False
        }
    ]

@mcp.tool()
def analyze_test_coverage(module_path: str) -> Dict[str, Any]:
    """
    Analyze the current test coverage for a given module and return missing test cases.
    Ideal for the Tester Agent to invoke after generating tests.
    
    Args:
        module_path: The path to the Python module or directory to analyze.
    """
    logger.info(f"Tool called: analyze_test_coverage for {module_path}")
    logger.debug("Simulating coverage analysis...")
    
    return {
        "module": module_path,
        "overall_coverage": "85%",
        "missing_lines": [12, 14, 45, 46],
        "recommendation": "Add edge-case tests for null Room Type assignment."
    }

if __name__ == "__main__":
    logger.info("Starting up FastMCP server manually...")
    mcp.run()
