How do I install the MCP Converter skill?

Run npx skillmds add majiayu000/mcp-converter in your terminal (requires Node.js), paste this page's agent-chat prompt into Claude, Cursor, or any MCP-connected agent, or download the SKILL.md file and copy it into your agent's skills directory.

Is MCP Converter safe to use?

This skill has not completed SkillMD's automated safety review yet. Capability flags: makes network calls, reads secrets. SkillMD never runs a skill's scripts for you; review the SKILL.md before installing.

Which AI agents work with MCP Converter?

This skill is tagged as working with Claude Code, Claude.ai, OpenAI Codex. SKILL.md is an open format, so most agents that read a skills directory can load it too.

Is MCP Converter free to use?

Yes. Installing skills from SkillMD is free, and the skill stays under its author's original license.

Who published MCP Converter?

majiayu000 (@majiayu000) published this skill. Their other Agent Skills are listed on their SkillMD profile.

MCP Converter by majiayu000 — Agent Skill

Name: MCP Converter
Rating: 4.2 (5 reviews)
Author: majiayu000

File contents

MCP-to-Skill Converter

Installation

The skill invokes .claude/tools/integrations/mcp-converter/batch_converter.py. Requirements:

Python 3.10+: python.org or winget install Python.Python.3.12 (Windows), brew install python@3.12 (macOS).
pip: Usually included with Python; verify with pip --version.
Dependencies: From the repo root, install deps for the integration (e.g. PyYAML if required):
```
pip install pyyaml
```
Run from project root; the script uses .claude/tools/integrations/mcp-converter/ (catalog: mcp-catalog.yaml).

Cheat Sheet & Best Practices

MCP design: Single responsibility per server; bounded toolsets; contracts first (strict I/O schemas); stateless by default; additive changes; security (identity, auth, audit). Prefer stdio for local, Streamable HTTP for remote; use a gateway for multi-tenant/centralized policy.

Conversion: Introspect server; estimate token usage of tool schemas; generate skill with progressive disclosure. Test converted skills before relying on them. Use catalog + batch_converter for rules-driven conversion.

Hacks: Focus on high-token or high-value servers first. Keep generated SKILL.md and wrappers in version control. Use mcp-catalog.yaml to mark keep_as_mcp or auto-convert thresholds.

Certifications & Training

MCP: MCP Best Practices, modelcontextprotocol.info. Skill data: Single responsibility, bounded tools, contracts first, stateless; stdio vs HTTP; gateway pattern; introspect → generate skill.

Hooks & Workflows

Suggested hooks: Post–MCP config change: optional batch_converter run to refresh skills. Use with evolution-orchestrator (add mcp-converter to secondary) when creating skills from MCP servers.

Workflows: Use with evolution-orchestrator. Flow: list servers → convert server or batch → test converted skill. See creators/skill-creator-workflow.yaml; mcp-converter feeds skill-creator input.

🚀 Usage

1. List Available MCP Servers

See which servers are configured in your .mcp.json:

python .claude/tools/mcp-converter/mcp_analyzer.py --list

2. Convert a Server

Convert a specific MCP server to a Skill:

python .claude/tools/mcp-converter/mcp_analyzer.py --server <server_name>

3. Batch Conversion (Catalog)

Convert multiple servers based on rules:

python .claude/tools/mcp-converter/batch_converter.py

ℹ️ How it Works

Introspect: Connects to the running MCP server.
Analyze: Estimates token usage of tool schemas.
Generate: Creates a SKILL.md wrapper that creates dynamic tool calls only when needed.

🔧 Dependencies

Requires mcp python package:

pip install mcp

Memory Protocol (MANDATORY)

Before starting: Read .claude/context/memory/learnings.md

After completing:

New pattern -> .claude/context/memory/learnings.md
Issue found -> .claude/context/memory/issues.md
Decision made -> .claude/context/memory/decisions.md

ASSUME INTERRUPTION: If it's not in memory, it didn't happen.

---name: mcp-converterdescription: Converts MCP servers to Claude Skills to save tokens. Runs the introspection tool to generate skill wrappers.--- # MCP-to-Skill Converter ## Installation The skill invokes `.claude/tools/integrations/mcp-converter/batch_converter.py`. Requirements: - **Python 3.10+**: [python.org](https://www.python.org/downloads/) or `winget install Python.Python.3.12` (Windows), `brew install python@3.12` (macOS). - **pip**: Usually included with Python; verify with `pip --version`. - **Dependencies**: From the repo root, install deps for the integration (e.g. PyYAML if required): ```bash pip install pyyaml ``` Run from project root; the script uses `.claude/tools/integrations/mcp-converter/` (catalog: `mcp-catalog.yaml`). ## Cheat Sheet & Best Practices **MCP design:** Single responsibility per server; bounded toolsets; contracts first (strict I/O schemas); stateless by default; additive changes; security (identity, auth, audit). Prefer stdio for local, Streamable HTTP for remote; use a gateway for multi-tenant/centralized policy. **Conversion:** Introspect server; estimate token usage of tool schemas; generate skill with progressive disclosure. Test converted skills before relying on them. Use catalog + batch_converter for rules-driven conversion. **Hacks:** Focus on high-token or high-value servers first. Keep generated SKILL.md and wrappers in version control. Use `mcp-catalog.yaml` to mark `keep_as_mcp` or auto-convert thresholds. ## Certifications & Training **MCP:** [MCP Best Practices](https://mcp-best-practice.github.io/mcp-best-practice/), [modelcontextprotocol.info](https://modelcontextprotocol.info/docs/best-practices/). **Skill data:** Single responsibility, bounded tools, contracts first, stateless; stdio vs HTTP; gateway pattern; introspect → generate skill. ## Hooks & Workflows **Suggested hooks:** Post–MCP config change: optional batch_converter run to refresh skills. Use with **evolution-orchestrator** (add mcp-converter to secondary) when creating skills from MCP servers. **Workflows:** Use with **evolution-orchestrator**. Flow: list servers → convert server or batch → test converted skill. See `creators/skill-creator-workflow.yaml`; mcp-converter feeds skill-creator input. ## 🚀 Usage ### 1. List Available MCP Servers See which servers are configured in your `.mcp.json`: ```bash python .claude/tools/mcp-converter/mcp_analyzer.py --list ``` ### 2. Convert a Server Convert a specific MCP server to a Skill: ```bash python .claude/tools/mcp-converter/mcp_analyzer.py --server <server_name> ``` ### 3. Batch Conversion (Catalog) Convert multiple servers based on rules: ```bash python .claude/tools/mcp-converter/batch_converter.py ``` ## ℹ️ How it Works 1. **Introspect**: Connects to the running MCP server. 2. **Analyze**: Estimates token usage of tool schemas. 3. **Generate**: Creates a `SKILL.md` wrapper that creates dynamic tool calls only when needed. ## 🔧 Dependencies Requires `mcp` python package: ```bash pip install mcp ``` ## Memory Protocol (MANDATORY) **Before starting:** Read `.claude/context/memory/learnings.md` **After completing:** - New pattern -> `.claude/context/memory/learnings.md` - Issue found -> `.claude/context/memory/issues.md` - Decision made -> `.claude/context/memory/decisions.md` > ASSUME INTERRUPTION: If it's not in memory, it didn't happen.

Frequently asked questions