BigBugAI MCP (Python + FastMCP)

License: MIT

Production-ready MCP server exposing BigBugAI tools with stdio transport for local MCP clients (Claude Desktop, Cursor) and optional HTTP/SSE transport (FastAPI + uvicorn) for remote access.

Auth via API key (env var)
Per-key rate limiting (moving window; configurable via env)
Typed Pydantic schemas for tool I/O
Clean error handling and JSON-stable outputs
Tests, ruff, and mypy configuration

Tools

get_trending_tokens(limit: int = 10) -> list[dict]
- GET ${BTUNIFIED_API}/api/tokens/newly-ingested by default
- Falls back to ${BTUNIFIED_API}/v1/trending/tokens and a few other candidates if 404
- Override primary path with BTUNIFIED_TRENDING_PATH
- Normalizes {items: [...]} -> [...]
token_analysis_by_contract(chain: str, address: str) -> dict
- GET ${BTUNIFIED_API}/api/token-intel/{chain}/{address}/report

Requirements

Python 3.11+
Packages: mcp[cli], httpx, pydantic, fastapi, uvicorn, limits, pytest, ruff, mypy

Environment variables

BIGBUGAI_MCP_API_KEY (required)
BIGBUGAI_API_KEY / BIGBUGAI_API_TOKEN (optional; used for upstream HTTP calls if set. If not set, HTTP calls will fall back to BIGBUGAI_MCP_API_KEY.)
BTUNIFIED_API (default: https://api.bigbug.ai)
MCP_RATE_LIMIT (default: 60/hour, rate string per limits)

Install

Using uv (recommended):

Unix/macOS

uv venv source .venv/bin/activate uv pip install -e .[dev]

Windows PowerShell

uv venv .venv\Scripts\Activate.ps1 uv pip install -e .[dev]

Alternatively, you can explicitly add packages (will update pyproject as needed):

uv add "mcp[cli]" httpx pydantic fastapi uvicorn limits pytest ruff mypy

Run (STDIO)

export BIGBUGAI_MCP_API_KEY="your-secret" export BTUNIFIED_API="https://api.bigbug.ai" uv run -m bigbugai_mcp.server_stdio

Windows PowerShell:

$env:BIGBUGAI_MCP_API_KEY="your-secret" $env:BTUNIFIED_API="https://api.bigbug.ai" uv run -m bigbugai_mcp.server_stdio

This mode is intended for local MCP clients (e.g., Claude Desktop, Cursor).

Note: Tools no longer require api_key in the payload. The server reads the API key from the environment (BIGBUGAI_MCP_API_KEY) and applies rate limiting based on it.

Claude Desktop config

Create claude_desktop_config.json:

{ "mcpServers": { "bigbugai": { "command": "uv", "args": ["-m", "bigbugai_mcp.server_stdio"], "env": { "BIGBUGAI_MCP_API_KEY": "your-secret", "BTUNIFIED_API": "https://api.bigbug.ai", "MCP_RATE_LIMIT": "60/hour" } } } }

Run (HTTP)

uv run -m bigbugai_mcp.server_http # server on :8000 curl -s http://localhost:8000/healthz

Expected output:

MCP HTTP/SSE endpoints are mounted under /mcp. Depending on your FastMCP version, an SSE stream may be available at /mcp/sse.

Example cURL (SSE; may require -N to keep the connection open and is primarily for debugging):

curl -N http://localhost:8000/mcp/sse

Note: MCP over HTTP/SSE is designed for compatible clients; manual cURL interaction is limited.

Smoke scripts

Quick sanity checks for the tools (require an API key in env):

Trending
# uses BIGBUGAI_API_KEY/BIGBUGAI_API_TOKEN/BIGBUGAI_MCP_API_KEY from env uv run python scripts/smoke_trending.py -l 5
Token analysis
# requires BIGBUGAI_MCP_API_KEY in env; optionally set BB_CHAIN/BB_ADDRESS $env:BIGBUGAI_MCP_API_KEY="your-secret" # PowerShell uv run python scripts/smoke_token_analysis.py

Testing and Quality

# Run unit tests uv run pytest -q # Lint uv run ruff check . # Type check uv run mypy src

Project layout

bigbugai-mcp/ .github/workflows/ci.yml CODE_OF_CONDUCT.md CONTRIBUTING.md LICENSE README.md SECURITY.md pyproject.toml scripts/list_tools.py scripts/smoke_token_analysis.py scripts/smoke_trending.py src/bigbugai_mcp/__init__.py src/bigbugai_mcp/auth.py src/bigbugai_mcp/models.py src/bigbugai_mcp/server_http.py src/bigbugai_mcp/server_stdio.py src/bigbugai_mcp/tools.py tests/test_tools.py

Security

Rotate API keys regularly
Keep HTTP mode behind OAuth/reverse proxy if exposed publicly
Rate limits are per API key in a moving window strategy

See SECURITY.md for reporting vulnerabilities.

Extending

Add more tools for BigBugAI endpoints (portfolio manager, investment suggester, etc.).

Add new Pydantic request/response models in src/bigbugai_mcp/models.py
Add the tool function in src/bigbugai_mcp/tools.py
Decorate with @guarded and register in register_tools()
Write tests in tests/

Contributing

Please see CONTRIBUTING.md for guidelines.

Code of Conduct

This project follows the Contributor Covenant.

License

MIT — see LICENSE.

BigBugAI MCP Server