mcp-kql-server 2.0.5

AI-Powered MCP server for KQL query execution with intelligent schema memory and context assistance

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.

🚀 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

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:#4a90e2,stroke:#2c5282,stroke-width:2px,color:#ffffff
    style B fill:#7c7c7c,stroke:#4a4a4a,stroke-width:2px,color:#ffffff
    style C fill:#e74c3c,stroke:#c0392b,stroke-width:2px,color:#ffffff
    style D fill:#8e44ad,stroke:#6a1b99,stroke-width:2px,color:#ffffff
    style E fill:#7c7c7c,stroke:#4a4a4a,stroke-width:2px,color:#ffffff
    style F fill:#27ae60,stroke:#1e8449,stroke-width:2px,color:#ffffff
    style G fill:#f39c12,stroke:#d68910,stroke-width:2px,color:#ffffff
    style H fill:#2980b9,stroke:#1f618d,stroke-width:2px,color:#ffffff
    style I fill:#f39c12,stroke:#d68910,stroke-width:2px,color:#ffffff
    style J fill:#7c7c7c,stroke:#4a4a4a,stroke-width:2px,color:#ffffff
    style K fill:#e74c3c,stroke:#c0392b,stroke-width:2px,color:#ffffff
    style L fill:#27ae60,stroke:#1e8449,stroke-width:2px,color:#ffffff
    style M fill:#8e44ad,stroke:#6a1b99,stroke-width:2px,color:#ffffff
    style N fill:#27ae60,stroke:#1e8449,stroke-width:2px,color:#ffffff
    style O fill:#f39c12,stroke:#d68910,stroke-width:2px,color:#ffffff

Schema Memory Discovery Flow

The kql_schema_memory functionality is now seamlessly integrated into the kql_execute tool. When you run a query, the server automatically discovers and caches the schema for any tables it hasn't seen before. This on-demand process ensures you always have the context you need without any manual steps.

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:#4a90e2,stroke:#2c5282,stroke-width:2px,color:#ffffff
    style B fill:#8e44ad,stroke:#6a1b99,stroke-width:2px,color:#ffffff
    style C fill:#f39c12,stroke:#d68910,stroke-width:2px,color:#ffffff
    style D fill:#2980b9,stroke:#1f618d,stroke-width:2px,color:#ffffff
    style E fill:#7c7c7c,stroke:#4a4a4a,stroke-width:2px,color:#ffffff
    style F fill:#e67e22,stroke:#bf6516,stroke-width:2px,color:#ffffff
    style G fill:#8e44ad,stroke:#6a1b99,stroke-width:2px,color:#ffffff
    style H fill:#f39c12,stroke:#d68910,stroke-width:2px,color:#ffffff
    style I fill:#2980b9,stroke:#1f618d,stroke-width:2px,color:#ffffff
    style J fill:#27ae60,stroke:#1e8449,stroke-width:2px,color:#ffffff

📋 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

git clone https://github.com/4R9UN/mcp-kql-server.git && cd mcp-kql-server && pip install -e .

Alternative Installation Methods

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

{
  "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

{
  "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

{
  "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:

# 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:

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

🔧 Quick Start

1. Authenticate with Azure (One-time setup)

az login

2. Start the MCP Server (Zero configuration)

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

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:#4a90e2,stroke:#2c5282,stroke-width:3px,color:#ffffff
    style B fill:#8e44ad,stroke:#6a1b99,stroke-width:3px,color:#ffffff
    style C fill:#e67e22,stroke:#bf6516,stroke-width:3px,color:#ffffff
    style D fill:#27ae60,stroke:#1e8449,stroke-width:3px,color:#ffffff

📁 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
│   ├── 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

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

Force Schema Refresh

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

Performance Optimization

{
    "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

   # Re-authenticate with Azure CLI
   az login --tenant your-tenant-id
   

2. Memory Issues

   # The memory cache is now managed automatically. If you suspect issues,
   # you can clear the cache directory, and it will be rebuilt on the next query.
   # Windows:
   rmdir /s /q "%APPDATA%\KQL_MCP\cluster_memory"
   
   # macOS/Linux:
   rm -rf ~/.local/share/KQL_MCP/cluster_memory
   

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.

📞 Support

• Issues: GitHub Issues
• PyPI Package: PyPI Project Page
• Author: Arjun Trivedi
• Certified : MCPHub

🌟 Star History

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Happy Querying! 🎉