token-risk-check
MCP server that evaluates memecoin tokens for scam/rug-pull risk, providing a verdict and risk report using live data from RugCheck and DexScreener.
README
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)
- Push this folder to a GitHub repo.
- Sign up at https://smithery.ai, connect GitHub, add the repo.
- Smithery builds the
Dockerfile(seesmithery.yaml), runs the server, and gives you a permanent public MCP URL — free. - 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.
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