Can I secure my MCP tools with this skill?

Yes, the skill provides guidance on implementing OAuth authentication and CEL (Common Expression Language) policies for granular authorization.

How does MXCP handle data ingestion?

MXCP uses DuckDB to read files (CSV, Parquet, JSON) and connect to external databases like PostgreSQL or MySQL, often paired with dbt for data quality tests.

What is the recommended development workflow?

The MXCP methodology emphasizes a 'validate constantly' approach, running 'mxcp validate' after every file change to ensure schema compliance and metadata quality.

MXCP (Model Context Protocol eXtension Platform) is an enterprise-ready framework for building MCP servers that support SQL and Python endpoints, security, and testing.

Does it support local and production deployment?

Yes, it supports stdio for local development in environments like Claude Desktop and streamable-http for production-scale web applications.

MXCP Framework Expert

Name: MXCP Framework Expert
Author: raw-labs

byraw-labs

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Desarrollo de API

Builds and deploys production-ready MCP servers using the MXCP enterprise framework with SQL and Python.

Acerca de

MXCP Framework Expert is a specialized skill for architecting high-performance Model Context Protocol (MCP) servers. It provides an opinionated methodology for creating tools, resources, and prompts using the MXCP framework, which integrates DuckDB for data access and dbt for transformations. This skill guides developers through the entire lifecycle of AI tool development—from initial project bootstrapping and SQL/Python endpoint implementation to advanced security configuration with OAuth and enterprise-grade deployment using streamable-http transports.

Características Principales

Automated data ingestion and transformation using DuckDB and dbt
Built-in validation, linting, and testing workflows for AI tools
Expert implementation of SQL and Python-based MCP endpoints
2 GitHub stars
Deployment guidance for both local stdio and production HTTP transports
Enterprise security configuration including OAuth and CEL policies

Casos de Uso

Building a production-grade MCP server to interface between an LLM and legacy enterprise databases
Creating secure, policy-driven message templates and resources for AI agents
Developing complex data analysis tools using Python logic and DuckDB extensions

Acerca de

Características Principales

Automated data ingestion and transformation using DuckDB and dbt
Built-in validation, linting, and testing workflows for AI tools
Expert implementation of SQL and Python-based MCP endpoints
2 GitHub stars
Deployment guidance for both local stdio and production HTTP transports
Enterprise security configuration including OAuth and CEL policies

Casos de Uso

Building a production-grade MCP server to interface between an LLM and legacy enterprise databases
Creating secure, policy-driven message templates and resources for AI agents
Developing complex data analysis tools using Python logic and DuckDB extensions