token-risk-check — MCP server

A Model Context Protocol (MCP) server that exposes one sellable tool: analyze_token_risk — a live scam/rug-pull check for any memecoin token.

AI agents call it before buying or promoting a token, and get back:

• a verdict (PASS / REJECT / UNAVAILABLE)
• reasons & warnings (honeypot pattern, age, buy pressure, missing socials…)
• a RugCheck risk report for Solana (score, top-holder %, mint/freeze authority)
• live market data from DexScreener (price, liquidity, volume, buys/sells…)

All data sources are free and keyless (DexScreener API + RugCheck API).

Tool

Tool	Description
analyze_token_risk(contract_address, chain="solana", min_buys_5m=50)	Assess whether a token is likely a scam or rug pull.

Run locally

pip install -r requirements.txt
python mcp_server.py

Endpoint: http://localhost:8000/mcp (streamable HTTP, stateless — no session handshake needed). Test it:

curl -X POST http://localhost:8000/mcp \
  -H "Content-Type: application/json" \
  -H "Accept: application/json, text/event-stream" \
  -d '{"jsonrpc":"2.0","id":1,"method":"tools/list","params":{}}'

Deploy for free on Smithery (always-on, no server to run)

1. Push this folder to a GitHub repo.
2. Sign up at https://smithery.ai, connect GitHub, add the repo.
3. Smithery builds the Dockerfile (see smithery.yaml), runs the server, and gives you a permanent public MCP URL — free.
4. Use that URL in any MCP client, or submit it to marketplaces (MCP-Hive, Glama, mcp.so) to sell the tool per invocation.

The PORT env var is set by Smithery at runtime; mcp_server.py reads it (8000 locally).

This folder is self-contained — it includes its own copies of the bot's data + scam modules (config.py, dexscreener.py, scam_filter.py). The token-risk check is for entertainment/education only; new coins are overwhelmingly scams. Not financial advice.