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MCP KQL Server

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from 4R9UN

Execute KQL queries using Azure authentication. Requires Azure CLI login.

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MCP KQL Server

mcp-name: io.github.4R9UN/mcp-kql-server

MseeP.ai Security Assessment Badge

AI-Powered KQL Query Execution with Natural Language to KQL (NL2KQL) Conversion and Execution

A Model Context Protocol (MCP) server that transforms natural language questions into optimized KQL queries with intelligent schema discovery, AI-powered caching, and seamless Azure Data Explorer integration. Simply ask questions in plain English and get instant, accurate KQL queries with context-aware results.

Latest Version: v2.1.4 - Canonical schema type normalization for sharper NL2KQL accuracy, credential redaction in logs, leaner dependencies, and a single-source package version.

Verified on MseeP MCP Registry PyPI version Python

CI/CD Pipeline codecov Security Rating Code Quality

FastMCP Azure Data Explorer MCP Protocol Maintenance MCP Badge

🎬 Demo

Watch a quick demo of the MCP KQL Server in action:

MCP KQL Server Demo

🚀 Features

  • execute_kql_query:

    • Natural Language to KQL: Generate KQL queries from natural language descriptions.
    • Direct KQL Execution: Execute raw KQL queries.
    • Multiple Output Formats: Supports JSON, CSV, and table formats.
    • Strict Schema Validation: Uses discovered schema memory and validation before execution.
    • Schema-Grounded Repair: Repairs invalid columns only when a valid table schema can prove the replacement.

  • kql_schema_memory:

    • Schema Discovery: Discover and cache schemas for tables.
    • Database Exploration: List all tables within a database.
    • AI Context: Get ranked CAG context for tables, with optional table-scoped strict schema output.
    • Analysis Reports: Generate reports with visualizations.
    • Cache Management: Clear or refresh the schema cache.
    • Memory Statistics: Get statistics about the memory usage.

📊 MCP Tools 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 schema memory flow is integrated into query execution, but it now reuses existing cached schema before attempting live discovery. If a table schema is already available in CAG/schema memory, the server will use that cached schema instead of re-indexing it.

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

🎯 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

%%{init: {'theme':'dark', 'themeVariables': {
  'primaryColor':'#1a1a2e',
  'primaryTextColor':'#00d9ff',
  'primaryBorderColor':'#00d9ff',
  'secondaryColor':'#16213e',
  'secondaryTextColor':'#c77dff',
  'secondaryBorderColor':'#c77dff',
  'tertiaryColor':'#0f3460',
  'tertiaryTextColor':'#ffaa00',
  'tertiaryBorderColor':'#ffaa00',
  'lineColor':'#00d9ff',
  'textColor':'#ffffff',
  'mainBkg':'#0a0e27',
  'nodeBorder':'#00d9ff',
  'clusterBkg':'#16213e',
  'clusterBorder':'#9d4edd',
  'titleColor':'#00ffff',
  'edgeLabelBackground':'#1a1a2e',
  'fontFamily':'Inter, Segoe UI, sans-serif',
  'fontSize':'16px',
  'flowchart':{'nodeSpacing':60, 'rankSpacing':80, 'curve':'basis', 'padding':20}
}}}%%
graph LR
    Client["🖥️ MCP Client<br/><b>Claude / AI / Custom</b><br/>─────────<br/>Natural Language<br/>Interface"]
    
    subgraph Server["🚀 MCP KQL Server"]
        direction TB
        FastMCP["⚡ FastMCP<br/>Framework<br/>─────────<br/>MCP Protocol<br/>Handler"]
        NL2KQL["🧠 NL2KQL<br/>Engine<br/>─────────<br/>AI Query<br/>Generation"]
        Executor["⚙️ Query<br/>Executor<br/>─────────<br/>Validation &<br/>Execution"]
        Memory["💾 Schema<br/>Memory<br/>─────────<br/>AI Cache"]
        
        FastMCP --> NL2KQL
        NL2KQL --> Executor
        Executor --> Memory
        Memory --> Executor
    end
    
    subgraph Azure["☁️ Azure Services"]
        direction TB
        ADX["📊 Azure Data<br/>Explorer<br/>─────────<br/><b>Kusto Cluster</b><br/>KQL Engine"]
        Auth["🔐 Azure<br/>Identity<br/>─────────<br/>Device Code<br/>CLI Auth"]
    end
    
    %% Client to Server
    Client ==>|"📡 MCP Protocol<br/>stdio or streamable HTTP"| FastMCP
    
    %% Server to Azure
    Executor ==>|"🔍 Execute KQL<br/>Query & Analyze"| ADX
    Executor -->|"🔐 Authenticate"| Auth
    Memory -.->|"📥 Fetch Schema<br/>On Demand"| ADX
    
    %% Styling - Using cyberpunk palette
    style Client fill:#1a1a2e,stroke:#00d9ff,stroke-width:4px,color:#00ffff
    style FastMCP fill:#16213e,stroke:#c77dff,stroke-width:3px,color:#c77dff
    style NL2KQL fill:#1a1a40,stroke:#ffaa00,stroke-width:3px,color:#ffaa00
    style Executor fill:#16213e,stroke:#9d4edd,stroke-width:3px,color:#9d4edd
    style Memory fill:#0f3460,stroke:#00d9ff,stroke-width:3px,color:#00d9ff
    style ADX fill:#1a1a2e,stroke:#ff6600,stroke-width:4px,color:#ff6600
    style Auth fill:#16213e,stroke:#00ffff,stroke-width:2px,color:#00ffff
    
    style Server fill:#0a0e27,stroke:#9d4edd,stroke-width:3px,stroke-dasharray: 5 5
    style Azure fill:#0a0e27,stroke:#ff6600,stroke-width:3px,stroke-dasharray: 5 5

Report Generated by MCP-KQL-Server | ⭐ Star this repo on GitHub

📁 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

🔒 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

🤝 Contributing

We welcome contributions! Please do.

📞 Support

🌟 Star History

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mcp-name: io.github.4R9UN/mcp-kql-server

Happy Querying! 🎉