-
security-
license-
qualityEnables AI assistants to enhance their responses with relevant documentation through a semantic vector search, offering tools for managing and processing documentation efficiently.
Last updated -
22
43
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.tomlRuns
uv run mainto start the serverServer runs in
/appdirectory in containerDocs 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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