mxcp 0.1.1

RAW Model Context Protocol

MXCP: Instantly Serve Your Operational Data to LLMs — Safely

Transform your data into AI-ready interfaces in minutes, not months

✨ Why MXCP?

MXCP (Model eXecution + Context Protocol) is a developer-first tool that bridges the gap between your operational data and AI applications. It lets you:

• 🚀 Go from data to AI in minutes — Define interfaces in YAML + SQL, serve instantly
• 🔒 Keep control of your data — Run locally, with full observability and type safety
• 🎯 Build production-ready AI tools — Combine real-time data, caching, and business logic
• 🛠️ Use familiar tools — DuckDB for execution, dbt for modeling, Git for versioning

🚀 Quick Start

# Install globally
pip install mxcp

# Install with Vault support (optional)
pip install "mxcp[vault]"

# Or develop locally
git clone https://github.com/raw-labs/mxcp.git && cd mxcp
python -m venv .venv && source .venv/bin/activate
pip install -e .

Try the included Earthquakes example:

cd examples/earthquakes
mxcp serve

💡 Key Features

1. Declarative Interface Definition

# tools/summarize_earthquakes.yml
mxcp: "1.0.0"
tool:
  name: summarize_earthquakes
  description: "Summarize earthquakes for a given date"
  parameters:
    - name: date
      type: string
      format: date
      description: "Date to summarize earthquakes for"
  return:
    type: object
    properties:
      summary:
        type: string
        description: "Summary of earthquakes for the date"
  source:
    code: |
      SELECT 'Summary for ' || $date || ': ' || COUNT(*) || ' earthquakes' AS summary
      FROM earthquakes
      WHERE event_date = $date

• Type-safe — Strong typing for LLM safety and schema tracing
• Fast restart — Quick server restarts for development
• dbt integration — Directly use your dbt models in endpoints

2. Powerful Data Engine

• DuckDB-powered — Run instantly, with no infrastructure
• Rich integrations — PostgreSQL, Parquet, CSV, JSON, HTTP, S3, and more
• Full SQL support — Joins, filters, aggregations, UDFs

3. Production-Ready Features

• dbt integration — Use your data models directly
• Git-based workflow — Version control and collaboration
• Validation tools — Type checking, SQL linting, and testing
• Drift detection — Monitor schema and endpoint changes across environments

🛠️ Core Concepts

Tools, Resources, Prompts

Define your AI interface using MCP (Model Context Protocol) specs:

• Tools — Functions that process data and return results
• Resources — Data sources and caches
• Prompts — Templates for LLM interactions

Project Structure

your-project/
├── mxcp-site.yml    # Project configuration
├── tools/           # Tool definitions
├── resources/       # Data sources
├── prompts/         # LLM templates
└── models/          # (Optional) dbt transformations & caches

CLI Commands

mxcp serve           # Start local MCP server
mxcp list            # List all endpoints
mxcp validate        # Check types, SQL, and references
mxcp test            # Run endpoint tests
mxcp query           # Execute SQL queries
mxcp init            # Initialize new project
mxcp dbt-config      # Configure dbt integration
mxcp dbt             # Run dbt commands
mxcp drift-snapshot  # Create drift detection baseline
mxcp drift-check     # Check for schema and endpoint drift
mxcp log             # Query audit logs

🔌 LLM Integration

MXCP implements the Model Context Protocol (MCP), making it compatible with:

• Claude Desktop — Native MCP support
• OpenAI-compatible tools — Via MCP adapters
• Custom integrations — Using the MCP specification

For specific setup instructions, see:

• Earthquakes Example — Complete Claude Desktop setup walkthrough
• Integration Guide — Claude Desktop, OpenAI, mcp-cli, and custom integrations

📚 Documentation

• Overview — Core concepts and architecture
• Quickstart — Get up and running
• CLI Reference — Command-line tools
• Configuration — Project setup
• Plugins — Extend DuckDB with custom Python functions
• Authentication — OAuth authentication setup
• Policy Enforcement — Access control and data filtering
• Audit Logging — Enterprise-grade execution logging
• Type System — Data types and validation
• Integrations — Data sources and tools
• Drift Detection — Schema and endpoint change monitoring

🤝 Contributing

We welcome contributions! See our development guide to get started.

🧠 About

MXCP is developed by RAW Labs, combining the best of:

• dbt's modular data modeling
• DuckDB's speed and connectors
• Python MCP official server
• Modern AI-native workflows