QuantConnectMCP

Complete QuantConnect/Lean reference documentation MCP server with 1265 entries covering 100% of the official QuantConnect v2 documentation sitemap.

Quick Start | Tools | Configuration | Database | Development

One Command. Two Minutes. Fully Functional.

pip install qc-lean-mcp
qc-lean-mcp

That's it. On first run, the server auto-builds the ChromaDB vector store from shipped JSON data (takes 30-60 seconds for embedding model download + indexing). Subsequent runs start instantly.

What you get:

• 6 MCP tools for QuantConnect/Lean docs lookup, code validation, and template generation

• 1265 entries indexed in a local ChromaDB vector store (100% offline)

• Sub-millisecond hot cache for priority lookups

• Python AST validation for Lean algorithms

• Common Python.NET issue detection and auto-repair

• Works with Claude Desktop, Cursor, Windsurf, OpenCode, and any MCP client

What you need:

• Python 3.10+

• Any MCP-compatible AI client

Related MCP server: OmniDocs MCP

Why QuantConnectMCP?

AI coding assistants hallucinate Lean API syntax. The QuantConnect API is large (QCAlgorithm, Algorithm Framework, 200+ indicators, multiple asset classes) and constantly evolving. Models trained on older code get method signatures wrong.

QuantConnectMCP gives AI assistants authoritative, real-time access to the complete QuantConnect/Lean reference:

• 100% coverage: 1257/1257 official QuantConnect v2 docs sitemap URLs

• Semantic search: Vector embeddings find relevant docs by meaning, not keywords

• Code validation: qc_validate checks Python Lean algorithms for common mistakes

• Auto-repair: qc_repair fixes Python.NET bridge issues, missing self. prefix, etc.

• Code generation: qc_scaffold generates validated algorithm templates

• 100% local: No network calls at runtime (ChromaDB auto-builds from shipped data)

Tools

+---------------------------+----------------------------------------------+
| Tool                      | Description                                  |
+---------------------------+----------------------------------------------+
| qc_lookup(name, kind?)    | Get complete docs for a symbol by exact name |
| qc_search(query, ...)     | Semantic search across all docs              |
| qc_browse(namespace, ...) | Enumerate all members of a namespace         |
| qc_validate(code?, ...)   | Validate Python syntax for Lean algorithms   |
| qc_repair(code, context)  | Fix common Python.NET issues in Lean code    |
| qc_scaffold(kind, name)   | Generate algorithm/indicator/research template|
+---------------------------+----------------------------------------------+

Quick Start

Claude Desktop

Add to ~/Library/Application Support/Claude/claude_desktop_config.json:

{
  "mcpServers": {
    "qc-lean-mcp": {
      "command": "qc-lean-mcp"
    }
  }
}

Cursor / Windsurf / OpenCode

{
  "mcpServers": {
    "qc-lean-mcp": {
      "command": "qc-lean-mcp"
    }
  }
}

SSE (HTTP)

qc-lean-mcp
# Then connect to http://localhost:8080

Or with environment variables:

TRANSPORT=sse PORT=8080 qc-lean-mcp

Configuration

+-------------------------+---------------+--------------------------------+
| Env Var                 | Default       | Description                    |
+-------------------------+---------------+--------------------------------+
| TRANSPORT               | stdio         | Transport: stdio or sse        |
| PORT                    | 8080          | Port for SSE transport         |
| QC_DB_PATH              | ./qc_db       | ChromaDB path                  |
| QC_COLLECTION           | qc_reference  | ChromaDB collection name       |
| QC_EMBED_MODEL          | all-MiniLM-L6 | Sentence transformer model     |
| QC_MAX_RESULTS          | 100           | Max search results             |
| LOG_LEVEL               | INFO          | Logging level                  |
| LAZY_MODEL              | 0             | Skip model preload at startup  |
+-------------------------+---------------+--------------------------------+

Database

• 1265 entries (QuantConnect v2 docs)

• 1257/1257 sitemap URLs (100% coverage)

• 20 namespaces: cloud, indicators, writing-algorithms, cli, docs, datasets, research, securities, reality-modeling, local, engine, ai, framework, etc.

• Hot cache for sub-millisecond priority lookups

• Auto-built on first run from shipped JSON data (14MB)

To rebuild the database:

qc-lean-mcp build

Development

git clone https://github.com/TheFractalyst/QuantConnectMCP.git
cd QuantConnectMCP
pip install -e ".[dev]"
pytest tests/ -v

Re-index ChromaDB

python pipeline/merge_and_index.py --reset

Docker

docker build -t qc-lean-mcp .
docker run -p 8080:8080 -e TRANSPORT=sse qc-lean-mcp

Tech Stack

• FastMCP 3.0 - MCP server framework with FileSystemProvider auto-discovery

• ChromaDB - Local vector database for semantic search (cosine, 384-dim)

• SentenceTransformers - all-MiniLM-L6-v2 embedding model

• RapidFuzz - Fuzzy string matching for name lookups

• Loguru - Structured logging

Links

• fractalyst.dev - Portfolio

• QuantConnect Documentation - Official docs

• LEAN Engine - Open-source algorithmic engine

• @TheFractalyst - X / Twitter

License

MIT - see LICENSE