Dedalus MCP Documentation Server
Enables AI-powered querying and serving of markdown documentation with search, Q\&A capabilities, and document analysis. Built for the YC Agents Hackathon with OpenAI integration and rate limiting protection.
README
Dedalus MCP Documentation Server
An MCP server for serving and querying documentation with AI capabilities. Built for the YC Agents Hackathon.
Quick Start (Local Development)
# Install uv package manager (same as Dedalus uses)
brew install uv # or pip install uv
# Install dependencies
uv sync --no-dev
# Configure API keys for AI features
cp config/.env.example .env.local
# Edit .env.local and add your OpenAI API key
# Test
uv run python tests/test_server.py
# Run
uv run main
Deploy to Dedalus
What Dedalus Needs
pyproject.toml- Package configuration with dependenciesmain.py(root) - Entry point that Dedalus expectssrc/main.py- The actual MCP server codedocs/- Your documentation files
Deployment Steps
-
Set Environment Variables in Dedalus UI:
OPENAI_API_KEY- Your OpenAI API key (required for AI features)
-
Deploy:
dedalus deploy . --name "your-docs-server"
How Dedalus Runs Your Server
- Installs dependencies using
uv syncfrompyproject.toml - Runs
uv run mainto start the server - Server runs in
/appdirectory in container - Docs are served from
/app/docs
Features
- Serve markdown documentation
- Search across docs
- AI-powered Q&A (with OpenAI)
- Rate limiting (10 requests/minute) to protect API keys
- Ready for agent handoffs
Tools Available
list_docs()- List documentation filessearch_docs()- Search with keywordsask_docs()- AI answers from docsindex_docs()- Index documentsanalyze_docs()- Analyze for tasks
Documentation
See docs/ directory for:
License
MIT
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