skill-to-mcp

Turn a plain-English skill description into a working MCP server in seconds.

LICENSE tests/ tests/

skill-to-mcp is a CLI code generator that reads a SKILL.md file — a plain-English description of a tool — and scaffolds a complete, runnable MCP (Model Context Protocol) server with:

• ✅ Correct JSON-RPC schema (tool_name, description, inputSchema, outputSchema)
• ✅ Pydantic v2 models for type-safe input/output
• ✅ MCP server boilerplate (stdio or HTTP SSE transport)
• ✅ pyproject.toml ready for pip install
• ✅ Auto-generated README.md with usage instructions

📖 README.it.md

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Why?

Describing a tool in Markdown is more readable, versionable, and composable than hand-writing JSON-RPC schemas. skill-to-mcp bridges the gap between "I know what my tool does" and "I have a working MCP server."

Read the full motivation: SKILL.md might be a better abstraction than function calling

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Apify Actor

Run skill-to-mcp on the Apify platform:

docker build -t skill-to-mcp .
docker run -e DEEPSEEK_API_KEY=sk-... skill-to-mcp

The Apify Actor accepts the same inputs as the CLI via a web form: Skill Description (required), LLM Model, MCP Transport, Generate Example Implementation, and API Key.

Output is pushed to the Apify Dataset and a ZIP archive of all generated files is saved to the key-value store.

The Actor builds directly from this git repository via .actor/actor.json and the root Dockerfile (entry point: python -m skill_to_mcp.actor).

📖 .actor/input_schema.json

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Quick Start

1. Install

$pip install skillmd-to-mcp

2. Set your API key

$export DEEPSEEK_API_KEY="sk-your-key-here"

Get a key at platform.deepseek.com. Cost per generation: < $0.001 with DeepSeek.

OpenAI fallback: If you have an OpenAI key, set OPENAI_API_KEY instead. The CLI automatically tries both: DEEPSEEK_API_KEY first, then OPENAI_API_KEY.

3. Create a SKILL.md

# webpage-reader

Fetches a web page given a URL and returns:
- title: the page title
- description: the meta description
- body_text: visible text stripped of HTML
- links: list of the first 10 internal links

## Input

- url: string, required
- max_links: integer, default 10

4. Generate the MCP server

$skill-to-mcp generate SKILL.md

Output:

output/
└── webpage-reader/
    ├── server.py          # MCP server, runnable immediately
    ├── models.py          # Pydantic v2 input/output models
    ├── handler.py         # Tool logic (stub — implement here)
    ├── pyproject.toml     # Dependencies and metadata
    └── README.md          # Installation and usage guide

5. Implement & Run

cd output/webpage-reader
pip install -e .
# Edit handler.py to add your business logic
python server.py

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CLI Reference

skill-to-mcp generate SKILL.md [OPTIONS]

Arguments

Argument	Description
SKILL.md	Path to the skill description file

Options

Option	Short	Default	Description
--output-dir	-o	./output	Output directory for generated files
--model	-m	deepseek-v4-flash	LLM model for schema inference
--api-key	-k	$DEEPSEEK_API_KEY or $OPENAI_API_KEY	API key for the LLM provider. Falls back to OPENAI_API_KEY if DEEPSEEK_API_KEY is not set
--transport	-t	stdio	MCP transport: stdio or sse
--dry-run		false	Show inferred schema without generating files
--prompt-file	-p		Custom system prompt for LLM inference
--with-example-impl		false	Generate a working handler implementation via LLM
--templates-dir			Custom Jinja2 templates directory
--verbose	-v	false	Enable debug logging

Batch Processing

Process multiple SKILL.md files at once:

skill-to-mcp generate tool1.md tool2.md tool3.md -o ./my-tools
skill-to-mcp generate *.md --with-example-impl

Examples

# Basic usage
skill-to-mcp generate SKILL.md

# Preview the inferred schema (no files written)
skill-to-mcp generate SKILL.md --dry-run

# Use SSE transport for HTTP-based MCP hosts
skill-to-mcp generate SKILL.md --transport sse

# Custom output directory
skill-to-mcp generate SKILL.md -o ./my-mcp-tools

# Use a specific model
skill-to-mcp generate SKILL.md -m deepseek-v4-flash

# Custom LLM prompt
skill-to-mcp generate SKILL.md --prompt-file my_prompt.txt

# Verbose output for debugging
skill-to-mcp generate SKILL.md -v

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Architecture

flowchart LR
    A[SKILL.md] --> B[parser.py]
    B --> C[llm.py]
    C --> D[DeepSeek API]
    D --> E[validator.py]
    E -->|valid| F[renderer.py]
    E -->|invalid + retry| C
    F --> G[templates/]
    G --> H[output/]

Pipeline

1. skill_to_mcp/parser.py — Reads SKILL.md, extracts title and sections
2. skill_to_mcp/llm.py — Calls DeepSeek API (OpenAI-compatible) to infer JSON Schema
3. skill_to_mcp/validator.py — Validates schema (types, snake_case, required fields)
4. skill_to_mcp/renderer.py — Applies Jinja2 templates to generate Python code
5. skill_to_mcp/cli.py — Typer-based CLI with Rich output

LLM Prompt

The system prompt is transparent and overridable. View the default in skill_to_mcp/config.py or customize with --prompt-file.

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MCP Host Configuration

Claude Desktop

Add to claude_desktop_config.json:

{
  "mcpServers": {
    "my-tool": {
      "command": "python",
      "args": ["path/to/output/my-tool/server.py"]
    }
  }
}

Cursor IDE

Add to .cursor/mcp.json:

{
  "mcpServers": {
    "my-tool": {
      "command": "python",
      "args": ["path/to/output/my-tool/server.py"]
    }
  }
}

SSE Transport

For HTTP-based MCP hosts:

skill-to-mcp generate SKILL.md --transport sse
cd output/my-tool && python server.py
# MCP endpoint: http://127.0.0.1:8000/sse

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Development

Setup

git clone https://github.com/YOUR_USERNAME/skill-to-mcp
cd skill-to-mcp
pip install -e ".[dev]"

Run Tests

# All tests (99 total)
pytest tests/ -v

# End-to-end only
pytest tests/test_e2e.py -v

# With real API (requires DEEPSEEK_API_KEY)
DEEPSEEK_API_KEY="sk-..." skill-to-mcp generate tests/fixtures/simple_skill.md --dry-run

Code Quality

$ruff check skill_to_mcp/ tests/

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Tech Stack

Component	Technology
CLI Framework	Typer
Templates	Jinja2
LLM SDK	OpenAI Python (DeepSeek-compatible)
Validation	Pydantic v2
MCP SDK	modelcontextprotocol/python-sdk
Output	Rich
Testing	pytest + pytest-mock
Linting	ruff

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FAQ

Do I need to understand MCP protocol internals?

No. The generated server handles JSON-RPC, tool discovery, and schema validation. You only need to implement handler.py.

What if the LLM produces a wrong schema?

The validator catches type errors, invalid names, and missing fields. Retry is automatic (up to 3 attempts). Use --dry-run to preview before generating.

Can I use OpenAI instead of DeepSeek?

Yes. The CLI automatically falls back to OPENAI_API_KEY if DEEPSEEK_API_KEY is not set.

export OPENAI_API_KEY="sk-..."
skill-to-mcp generate SKILL.md --model gpt-4o-mini

Any OpenAI-compatible endpoint works (OpenAI, DeepSeek, OpenRouter, Together AI, etc.).

What Python version do I need?

Python 3.10+ for the CLI. The generated MCP servers also require Python 3.10+.

Is this production-ready?

This is an MVP (v0.1.0). The generated code is correct and runnable, but you should review and test the generated handler before deploying.

How much does each generation cost?

~$0.001 per generation with DeepSeek V4 Flash. The prompt is ~300 tokens and the response is ~200 tokens.

Is the --with-example-impl handler production-ready?

The LLM-generated handler is a starting point. It may contain bugs (e.g., referencing local variables instead of input.field). Always review and test the generated code before deploying. The prompt has been tuned to minimize common LLM mistakes, but human review is recommended.

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Roadmap

• --with-example-impl flag for LLM-generated handler implementations
• TypeScript/Go output support
• Batch processing (multiple SKILL.md files)
• Hosted registry at registry.skill-to-mcp.dev
• skill-to-mcp publish command
• Smithery integration
• Custom template directories (--templates-dir)

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Contributing

Pull requests are welcome! See project_state.md for current status and next steps.

1. Fork the repo
2. Create a feature branch (git checkout -b feature/amazing-feature)
3. Run tests (pytest tests/ -v)
4. Commit your changes
5. Push and open a PR

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License

MIT — see LICENSE.

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