mxcp-expert

Guide MXCP MCP server development from initialization through production deployment.

2|Updated Oct 20, 2025
One-click install
npx skills add https://github.com/raw-labs/claude-code-marketplace --skill mxcp-expert
Or copy as Structured Prompt for Agentโ–ผ
Please help me install this Agent Skill.
Skill: mxcp-expert
Source: https://github.com/raw-labs/claude-code-marketplace/tree/main/.claude-plugin/plugins/mxcp-expert/skills/mxcp-expert
Command: npx skills add https://github.com/raw-labs/claude-code-marketplace --skill mxcp-expert

SYSTEM DOCUMENTATION & REQUIREMENTS

๐Ÿ’ก This Skill requires yaml, jsonschema, and includes scripts (resource) and references (resource) and assets (resource) components.

What problem does it solve?

This Skill provides expert guidance for building production-ready MXCP servers and MCP tooling. It helps teams design secure, observable, and scalable AI toolchains, reducing risk and accelerating delivery.

Core Features & Use Cases

  • Production-ready guidance: Best practices for architecture, security, testing, and drift detection.
  • Project scaffolding: Clear guidance on structuring MXCP projects (tools, resources, prompts, python, sql, dbt, extensions).
  • Quality & governance: Guidance on validation, linting, evals, and audit trails to meet enterprise standards.
  • Use Case: Plan a multi-environment MXCP deployment with policy enforcement and observability from day one.

Quick Start

Start a new MXCP project with bootstrap, then refer to this Skill for production-grade design decisions. Review the MXCP documentation and templates to scaffold a compliant, auditable workflow.

Frequently Asked Questions about mxcp-expert

High-intent search queries and answers about installing and using this skill.

FAQPage Schema
How do I build a production-ready MCP server with security and compliance?โ–ผ

Production-ready MCP servers require architecture planning, security controls, and audit trails from the start. This Skill guides you through endpoint configuration, authentication setup, policy enforcement, and validation checkpoints to meet enterprise standards throughout development and deployment.

What's the best way to structure an MXCP project for scalability and observability?โ–ผ

Structure MXCP projects by organizing tools, resources, prompts, Python, SQL, and DBT components with clear separation of concerns. This Skill provides scaffolding patterns and documentation practices that enable drift detection, observability, and multi-environment deployments from day one.

How do I enforce MXCP methodology and maintain governance across my AI toolchain?โ–ผ

MXCP methodology enforcement starts with Step 0 task analysis and pre-implementation checklists, then applies validation, linting, and evals at each stage. This Skill ensures consistent documentation and tooling references across YAML, SQL, and Python to reduce risk and maintain audit compliance.

Can I use MXCP for multi-environment deployments with policy controls?โ–ผ

Yes. MXCP supports multi-environment deployments with built-in policy enforcement and observability. This Skill shows how to configure endpoints, enable authentication, apply policies, and test across environments while maintaining security and compliance requirements.

What dependencies do I need to set up an MXCP project?โ–ผ

MXCP projects depend on YAML for configuration and JSON Schema for validation. This Skill guides dependency integration, tooling setup, and project initialization so your MCP server meets production standards from bootstrap onward.

Why should I use a structured MXCP approach instead of building ad hoc?โ–ผ

Structured MXCP development reduces deployment risk, accelerates delivery, and ensures compliance through enforced validation, consistent tooling, and observable architecture. Ad hoc approaches lack auditability and drift detection, which production systems require.