If you're building autonomous trading agents on Solana, you've hit this problem: your agent needs to vet a token before it buys — but most agents trade blind. They have no idea whether a token is a rug pull, a honeypot, or has an active mint authority that can dilute holders to zero.
I built RugCheck AI to fix this: an MCP server that screens any SPL / Token-2022 token for the common scam patterns, then executes the swap through an MEV-protected route — screening and execution in one place.
What it checks
- Mint & freeze authority — is supply still mintable? Can your tokens be frozen in place?
- Honeypot detection — can the token actually be sold, or only bought?
- Liquidity & holders — concentration, LP locks, real depth.
- Token-2022 traps — permanent delegate, transfer hooks, default-frozen state.
Tools
-
verify_token_safety— full on-chain safety audit -
check_authorities— mint / freeze / Token-2022 trap detection -
simulate_sell— honeypot check -
execute_safe_swap— MEV-protected execution
Connect
RugCheck AI is listed on the official MCP Registry as io.github.MrWizardlyLoaf/rugcheck-ai. Remote endpoint, no install:
https://web-production-58d585.up.railway.app/mcp
Or self-host — it's open source (MIT) on GitHub: https://github.com/MrWizardlyLoaf/rugcheck-ai
Why pre-trade screening matters
A rug pull on Solana often looks fine at the moment of purchase — liquidity is there, the chart is green. The trap is in the authority: a live mint authority lets the deployer print unlimited supply after you buy; a freeze authority can lock your tokens so you can never sell. An autonomous agent that doesn't read these on-chain fields walks straight into it. RugCheck AI reads them directly and gives a verdict on a fresh launch instead of unknown.
Built for Solana trading agents. Feedback welcome.