MCP KQL Server

Name: mcp-kql-server
Author: 4R9UN

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

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: v2.1.2 - Hardcoded 10-minute Kusto servertimeout, ADX-side dry-run validation for generated queries, schema-drift recovery loop, and fully schema-driven NL2KQL with no hardcoded table or column names.

🎬 Demo

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

🆕 What's New in v2.1.2

⏱️ Hardcoded 10-min Query Timeout: Every Kusto call now ships ClientRequestProperties.servertimeout (capped at 600s). Long queries no longer silently die at the ADX default of ~4 minutes.
🔍 ADX-Side Dry-Run Validation: NL2KQL leader candidates are wrapped as <query> | take 0 and bound by ADX itself. Catches schema drift the cached validator misses, costs zero rows.
🔁 Schema-Drift Recovery Loop: On SEM0100 / "failed to resolve" failures the server refreshes the schema, repairs the query against real columns, and retries exactly once. No infinite loops.
🧭 Smarter Retry Policy: Server-side timeouts are no longer auto-retried (was burning 3× the budget). Only true transport failures (refused, reset, throttled, DNS, socket) retry.
🪪 Per-Request Trace IDs: Each Kusto call carries a unique client_request_id for cross-process correlation.
🧹 Schema-Driven Generation: Removed all hardcoded table, cluster, and column names from the NL2KQL pipeline. Time columns and join keys are derived from the live schema.
🧰 Cleanup: Removed legacy manual verification scripts; added pinned regression tests for timeout, error classifier, and dry-run.

🆕 What's New in v2.1.1

🎯 Schema-First CAG: KQL generation now ranks tables and columns from cached schema context before building queries.
🧠 Strict Table Context: schema_memory(operation="get_context") can be scoped to a specific table and returns allowed/recommended columns.
🩹 Schema-Grounded Repair: Invalid client-generated KQL can be repaired against real schema columns before execution.
💾 Safer Cache Isolation: Query-result cache is scoped by query, cluster, database, and output namespace.
♻️ No Redundant Reindexing: Existing cached schemas are reused and no longer overwritten by placeholder discovery paths.

See RELEASE_NOTES.md for full details.

🚀 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.
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

📋 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

One-time install (any platform):
pip install --upgrade mcp-kql-server
After install, prefer the mcp-kql-server console script in your client config. It is dropped on PATH by pip and bypasses the "which Python is python?" trap that VS Code's Python extension creates by silently substituting a cached interpreter path.

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": {
      "type": "stdio",
      "command": "mcp-kql-server",
      "args": []
    }
  }
}

Alternative: invoke via the Python module (Windows uses the py launcher)

{
  "mcpServers": {
    "mcp-kql-server": {
      "type": "stdio",
      "command": "py",
      "args": ["-3", "-m", "mcp_kql_server"]
    }
  }
}

On macOS / Linux replace "py" with "python3".

VSCode (with MCP Extension)

Add to your VSCode MCP configuration:

Settings.json location:

Windows: %APPDATA%\Code\User\mcp.json
macOS: ~/Library/Application Support/Code/User/mcp.json
Linux: ~/.config/Code/User/mcp.json

{
  "servers": {
    "mcp-kql-server": {
      "type": "stdio",
      "command": "mcp-kql-server",
      "args": []
    }
  }
}

If VS Code logs spawn ...PythonNNN/python.exe ENOENT, the Python extension is substituting a cached interpreter path for "python". Switch to the "mcp-kql-server" console script (above) or to "py" / "python3". Do not use the bare string "python" on Windows when VS Code's Python extension is installed.

Roo-code Or Cline (VS-code Extentions)

Ask or Add to your Roo-code Or Cline MCP settings:

MCP Settings location:

All platforms: Through Roo-code extension settings or mcp_settings.json

{
  "mcp-kql-server": {
    "type": "stdio",
    "command": "mcp-kql-server",
    "args": [],
    "alwaysAllow": []
  }
}

Generic MCP Client

For any MCP-compatible application:

# Preferred: console script installed by pip (cross-platform)
mcp-kql-server

# Equivalent module form (Windows uses the py launcher)
py -3 -m mcp_kql_server     # Windows
python3 -m mcp_kql_server   # macOS / Linux

# Server provides these tools:
# - execute_kql_query: Execute KQL or generate KQL from natural language
# - schema_memory: Discover, cache, and inspect cluster schemas

🔧 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:

execute_kql_query - Execute KQL queries or generate KQL from natural language

schema_memory - Discover, refresh, and inspect cached 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

%%{init: {'theme':'dark', 'themeVariables': {
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  'primaryBorderColor':'#00d9ff',
  'secondaryColor':'#16213e',
  'secondaryTextColor':'#c77dff',
  'secondaryBorderColor':'#c77dff',
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  'mainBkg':'#0a0e27',
  'nodeBorder':'#00d9ff',
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}}}%%
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/SSE"| 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

🚀 Production Deployment

Ready to deploy MCP KQL Server to Azure for production use? We provide comprehensive deployment automation for Azure Container Apps with enterprise-grade security and scalability.

🌟 Features

✅ Serverless Compute: Azure Container Apps with auto-scaling
✅ Managed Identity: Passwordless authentication with Azure AD
✅ Infrastructure as Code: Bicep templates for reproducible deployments
✅ Monitoring: Integrated Log Analytics and Application Insights
✅ Secure by Default: Network isolation, RBAC, and least-privilege access
✅ One-Command Deploy: Automated PowerShell and Bash scripts

📖 Deployment Guide

For complete deployment instructions, architecture details, and troubleshooting:

👉 View Production Deployment Guide

The guide includes:

🏗️ Detailed architecture diagrams
⚙️ Step-by-step deployment instructions (PowerShell & Bash)
🔒 Security configuration best practices
🐛 Troubleshooting common issues
📦 Docker containerization details

Quick Deploy

# PowerShell (Windows)
cd deployment
.\deploy.ps1 -SubscriptionId "YOUR_SUB_ID" -ResourceGroupName "mcp-kql-prod-rg" -ClusterUrl "https://yourcluster.region.kusto.windows.net"

# Bash (Linux/Mac/WSL)
cd deployment
./deploy.sh --subscription "YOUR_SUB_ID" --resource-group "mcp-kql-prod-rg" --cluster-url "https://yourcluster.region.kusto.windows.net"

📁 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

🐛 Troubleshooting

Common Issues

Authentication Errors

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

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\unified_memory.json"

# macOS/Linux:
rm -rf ~/.local/share/KQL_MCP/cluster_memory

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

🤝 Contributing

We welcome contributions! Please do.

📞 Support

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

🌟 Star History

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

Happy Querying! 🎉

MCP KQL Server

🎬 Demo

🆕 What's New in v2.1.2

🆕 What's New in v2.1.1

🚀 Features

📊 MCP Tools Execution Flow

Schema Memory Discovery Flow

📋 Prerequisites

🚀 One-Command Installation

Quick Install (Recommended)

From Source

Alternative Installation Methods

📱 MCP Client Configuration

Claude Desktop

VSCode (with MCP Extension)

Roo-code Or Cline (VS-code Extentions)

Generic MCP Client

🔧 Quick Start

1. Authenticate with Azure (One-time setup)

2. Start the MCP Server (Zero configuration)

3. Use via MCP Client

execute_kql_query - Execute KQL queries or generate KQL from natural language

schema_memory - Discover, refresh, and inspect cached cluster schemas

💡 Usage Examples

Basic Query Execution

Complex Analytics Query

Schema Discovery

Data Exploration with Context

Time-based Analysis

🎯 Key Benefits

For Data Analysts

For DevOps Teams

For AI Applications

🏗️ Architecture

🚀 Production Deployment

🌟 Features

📖 Deployment Guide

Quick Deploy

📁 Project Structure

🔒 Security

🐛 Troubleshooting

Common Issues

🤝 Contributing

📞 Support

🌟 Star History

Yorumlar (0)

`execute_kql_query` - Execute KQL queries or generate KQL from natural language

`schema_memory` - Discover, refresh, and inspect cached cluster schemas