# MCP KQL Server

> **AI-Powered KQL Query Execution with Intelligent Schema Memory**

A Model Context Protocol (MCP) server that provides intelligent KQL (Kusto Query Language) query execution with AI-powered schema caching and context assistance for Azure Data Explorer clusters.

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## 🚀 Features

- **🎯 Intelligent KQL Execution**: Execute KQL queries against any Azure Data Explorer cluster
- **🧠 AI Schema Memory**: Automatic schema discovery and intelligent caching
- **📊 Rich Visualizations**: Markdown table output with configurable formatting
- **⚡ Performance Optimized**: Smart caching reduces cluster API calls
- **🔐 Azure Authentication**: Seamless Azure CLI integration
- **🎨 Context-Aware**: AI-powered query assistance and error suggestions
- **🔕 Clean Output**: Suppressed FastMCP branding for professional experience (v2.0.2+)

## 📊 MCP Tools Execution Flow

### KQL Query Execution Flow

```mermaid
graph TD
    A[👤 User Submits KQL Query] --> B{🔍 Query Validation}
    B -->|❌ Invalid| C[📝 Syntax Error Response]
    B -->|✅ Valid| D[🧠 Load Schema Context]
    
    D --> E{💾 Schema Cache Available?}
    E -->|✅ Yes| F[⚡ Load from Memory]
    E -->|❌ No| G[🔍 Discover Schema]
    
    F --> H[🎯 Execute Query]
    G --> I[💾 Cache Schema + AI Context]
    I --> H
    
    H --> J{🎯 Query Success?}
    J -->|❌ Error| K[🚨 Enhanced Error Message]
    J -->|✅ Success| L[📊 Process Results]
    
    L --> M[🎨 Generate Visualization]
    M --> N[📤 Return Results + Context]
    
    K --> O[💡 AI Suggestions]
    O --> N
    
    style A fill:#e1f5fe
    style N fill:#e8f5e8
    style K fill:#ffebee
```

### Schema Memory Discovery Flow

```mermaid
graph TD
    A[👤 User Requests Schema Discovery] --> B[🔗 Connect to Cluster]
    B --> C[📂 Enumerate Databases]
    C --> D[📋 Discover Tables]
    
    D --> E[🔍 Get Table Schemas]
    E --> F[🤖 AI Analysis]
    F --> G[📝 Generate Descriptions]
    
    G --> H[💾 Store in Memory]
    H --> I[📊 Update Statistics]
    I --> J[✅ Return Summary]
    
    style A fill:#e1f5fe
    style J fill:#e8f5e8
```


## 📋 Prerequisites

- Python 3.10 or higher
- Azure CLI installed and authenticated (`az login`)
- Access to Azure Data Explorer cluster(s)

## 🚀 One-Command Installation

### Quick Install (Recommended)

#### From Source

```bash
git clone https://github.com/4R9UN/mcp-kql-server.git && cd mcp-kql-server && pip install -e .
```
### Alternative Installation Methods

```bash
pip install mcp-kql-server
```

**That's it!** The server automatically:
- ✅ Sets up memory directories in `%APPDATA%\KQL_MCP` (Windows) or `~/.local/share/KQL_MCP` (Linux/Mac)
- ✅ Configures optimal defaults for production use
- ✅ Suppresses verbose Azure SDK logs
- ✅ No environment variables required


## 📱 MCP Client Configuration

### Claude Desktop

Add to your Claude Desktop MCP settings file (`mcp_settings.json`):

**Location:**
- **Windows**: `%APPDATA%\Claude\mcp_settings.json`
- **macOS**: `~/Library/Application Support/Claude/mcp_settings.json`
- **Linux**: `~/.config/Claude/mcp_settings.json`

```json
{
  "mcpServers": {
    "mcp-kql-server": {
      "command": "python",
      "args": ["-m", "mcp_kql_server"],
      "env": {}
    }
  }
}
```

### VSCode (with MCP Extension)

Add to your VSCode MCP configuration:

**Settings.json location:**
- **Windows**: `%APPDATA%\Code\User\settings.json`
- **macOS**: `~/Library/Application Support/Code/User/settings.json`
- **Linux**: `~/.config/Code/User/settings.json`

```json
{
  "mcp.servers": {
    "mcp-kql-server": {
      "command": "python",
      "args": ["-m", "mcp_kql_server"],
      "cwd": null,
      "env": {}
    }
  }
}
```

### Roo-code (Cline Extension)

Add to your Roo-code MCP settings:

**MCP Settings location:**
- **All platforms**: Through Roo-code extension settings or `mcp_settings.json`

```json
{
  "mcpServers": {
    "kql-server": {
      "command": "python",
      "args": ["-m", "mcp_kql_server"],
      "env": {},
      "description": "KQL Server for Azure Data Explorer queries with AI assistance"
    }
  }
}
```

### Generic MCP Client

For any MCP-compatible application:

```bash
# Command to run the server
python -m mcp_kql_server

# Server provides these tools:
# - kql_execute: Execute KQL queries with AI context
# - kql_schema_memory: Discover and cache cluster schemas
```

### Configuration with Environment Variables

You can customize the server behavior with environment variables:

```json
{
  "mcpServers": {
    "mcp-kql-server": {
      "command": "python",
      "args": ["-m", "mcp_kql_server"],
      "env": {
        
      }
    }
  }
}
```
## 🔧 Quick Start

### 1. Authenticate with Azure (One-time setup)

```bash
az login
```

### 2. Start the MCP Server (Zero configuration)

```bash
python -m mcp_kql_server
```

The server starts immediately with:
- 📁 **Auto-created memory path**: `%APPDATA%\KQL_MCP\cluster_memory`
- 🔧 **Optimized defaults**: No configuration files needed
- 🔐 **Secure setup**: Uses your existing Azure CLI credentials

### 3. Use via MCP Client

The server provides two main tools:

> #### `kql_execute` - Execute KQL Queries with AI Context
> #### `kql_schema_memory` - Discover and Cache Cluster Schemas


## 💡 Usage Examples

### Basic Query Execution

Ask your MCP client (like Claude):
> "Execute this KQL query against the help cluster: `cluster('help.kusto.windows.net').database('Samples').StormEvents | take 10` and summarize the result and give me high level insights "

### Complex Analytics Query

Ask your MCP client:
> "Query the Samples database in the help cluster to show me the top 10 states by storm event count, include visualization"

### Schema Discovery

Ask your MCP client:
> "Discover and cache the schema for the help.kusto.windows.net cluster, then tell me what databases and tables are available"

### Data Exploration with Context

Ask your MCP client:
> "Using the StormEvents table in the Samples database on help cluster, show me all tornado events from 2007 with damage estimates over $1M"

### Time-based Analysis

Ask your MCP client:
> "Analyze storm events by month for the year 2007 in the StormEvents table, group by event type and show as a visualization"



## 🎯 Key Benefits

### For Data Analysts
- **⚡ Faster Query Development**: AI-powered autocomplete and suggestions
- **🎨 Rich Visualizations**: Instant markdown tables for data exploration
- **🧠 Context Awareness**: Understand your data structure without documentation

### For DevOps Teams
- **🔄 Automated Schema Discovery**: Keep schema information up-to-date
- **💾 Smart Caching**: Reduce API calls and improve performance
- **🔐 Secure Authentication**: Leverage existing Azure CLI credentials

### For AI Applications
- **🤖 Intelligent Query Assistance**: AI-generated table descriptions and suggestions
- **📊 Structured Data Access**: Clean, typed responses for downstream processing
- **🎯 Context-Aware Responses**: Rich metadata for better AI decision making

## 🏗️ Architecture

```mermaid
graph TD
    A[MCP Client<br/>Claude/AI/Custom] <--> B[MCP KQL Server<br/>FastMCP Framework]
    B <--> C[Azure Data Explorer<br/>Kusto Clusters]
    B <--> D[Schema Memory<br/>Local AI Cache]
    
    style A fill:#e1f5fe
    style B fill:#f3e5f5
    style C fill:#fff3e0
    style D fill:#e8f5e8
```

## 📁 Project Structure

```
mcp-kql-server/
├── mcp_kql_server/
│   ├── __init__.py          # Package initialization
│   ├── mcp_server.py        # Main MCP server implementation
│   ├── execute_kql.py       # KQL query execution logic
│   ├── schema_memory.py     # Schema caching and discovery
│   ├── unified_memory.py    # Advanced memory management
│   ├── kql_auth.py          # Azure authentication
│   ├── utils.py             # Utility functions
│   └── constants.py         # Configuration constants
├── docs/                    # Documentation
├── Example/                 # Usage examples
├── pyproject.toml          # Project configuration
└── README.md               # This file
```

## 🚀 Advanced Usage

### Custom Memory Path

```python
{
    "tool": "kql_execute",
    "input": {
        "query": "...",
        "cluster_memory_path": "/custom/memory/path"
    }
}
```

### Force Schema Refresh

```python
{
    "tool": "kql_schema_memory",
    "input": {
        "cluster_uri": "mycluster",
        "force_refresh": true
    }
}
```

### Performance Optimization

```python
{
    "tool": "kql_execute",
    "input": {
        "query": "...",
        "use_schema_context": false,  # Disable for faster execution
        "visualize": false           # Disable for minimal response
    }
}
```

## 🔒 Security

- **Azure CLI Authentication**: Leverages your existing Azure device login
- **No Credential Storage**: Server doesn't store authentication tokens
- **Local Memory**: Schema cache stored locally, not transmitted

## 🐛 Troubleshooting

### Common Issues

1. **Authentication Errors**
   ```bash
   # Re-authenticate with Azure CLI
   az login --tenant your-tenant-id
   ```

2. **Memory Issues**
   ```bash
   # Clear schema cache if corrupted (automatic backup created)
   # Windows:
   del "%APPDATA%\KQL_MCP\schema_memory.json"
   
   # macOS/Linux:
   rm ~/.local/share/KQL_MCP/schema_memory.json
   ```

3. **Connection Timeouts**
   - Check cluster URI format
   - Verify network connectivity
   - Confirm Azure permissions

4. **Memory Path Issues**
   - Server automatically creates fallback directory in `~/.kql_mcp_memory` if default path fails
   - Check logs for memory path initialization messages

## 🤝 Contributing

We welcome contributions! Please do. 

## 🙏 Acknowledgments

- [FastMCP](https://github.com/jlowin/fastmcp) - MCP server framework
- [Azure Kusto Python SDK](https://github.com/Azure/azure-kusto-python) - KQL client library
- [Model Context Protocol](https://github.com/anthropics/mcp) - Protocol specification
- [Microsoft Azure Data Explorer](https://azure.microsoft.com/en-us/services/data-explorer/) - Cloud analytics service

## 📞 Support

- **Issues**: [GitHub Issues](https://github.com/4R9UN/mcp-kql-server/issues)
- **Discussions**: [GitHub Discussions](https://github.com/4R9UN/mcp-kql-server/discussions)
- **Documentation**: [Full Documentation](https://github.com/4R9UN/mcp-kql-server/docs)
- **PyPI Package**: [PyPI Project Page](https://pypi.org/project/mcp-kql-server/)
- **Author**: [Arjun Trivedi](mailto:arjuntrivedi42@yahoo.com)
- **Certified** : [MCPHub](https://mcphub.com/mcp-servers/4R9UN/mcp-kql-server)

## 🌟 Star History

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---

**Happy Querying! 🎉**
