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 dependencies
main.py (root) - Entry point that Dedalus expects
src/main.py - The actual MCP server code
docs/ - 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 sync from pyproject.toml
Runs uv run main to start the server
Server runs in /app directory 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 files
search_docs() - Search with keywords
ask_docs() - AI answers from docs
index_docs() - Index documents
analyze_docs() - Analyze for tasks

Documentation

See docs/ directory for:

License

MIT

Deploy Server

HTTP connection URL

security - not tested

license - permissive license

quality - not tested

How are these scores calculated?

hybrid server

The server is able to function both locally and remotely, depending on the configuration or use case.

Tools

Enables AI-powered querying and management of documentation through markdown file serving, keyword search, and OpenAI-based Q&A capabilities. Supports document indexing, analysis, and agent handoffs with rate limiting protection.

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