rug-check
Server Configuration
Describes the environment variables required to run the server.
| Name | Required | Description | Default |
|---|---|---|---|
No arguments | |||
Instructions
Guidance the server publishes about itself, which clients place ahead of the tool catalog so the model reads it before choosing anything.
This server publishes no instructions, or was last inspected before Glama recorded them.
Capabilities
Features and capabilities supported by this server
Protocol revision2025-11-25
| Capability | Details |
|---|---|
| tools | {} |
Tools
Functions exposed to the LLM to take actions
| Name | Description |
|---|---|
| rug_check_tokenA | Assess whether an ERC-20 token is a likely SCAM / rug pull BEFORE buying or interacting with it. Returns a risk score (0-100) and verdict (SAFE / CAUTION / HIGH RISK) built from LIVE on-chain reads: dangerous owner powers found in the token's deployed bytecode (mint, blacklist, pause, set-fee, set-max-tx), whether ownership is renounced, what % of supply the owner holds, whether a DEX liquidity pair exists and how deep it is, and whether the LP tokens are locked/burned (so the deployer can't pull liquidity). Use this whenever a user or agent is about to trade, approve, or add liquidity for an unfamiliar token. Chains: ethereum, base, bsc, polygon, arbitrum. |
| rug_check_manyA | Run rug_check on up to 10 tokens at once. Pass an array of {chain,address}. Returns the verdict and score for each so an agent can screen a watchlist or a batch of candidate tokens in one call. |
Prompts
Interactive templates invoked by user choice
| Name | Description |
|---|---|
No prompts | |
Resources
Contextual data attached and managed by the client
| Name | Description |
|---|---|
No resources | |
TDQS
Scored across 2 tools
The two tools have clearly distinct purposes: one for batch checking up to 10 tokens, the other for individual token assessment. No overlap.
Both tools follow a consistent 'rug_check_' prefix with descriptive verbs ('many' vs 'token'), all in snake_case.
With only 2 tools, the count is minimal but perfectly scoped for the domain of rug checking, covering both individual and batch needs.
The tool set covers the primary use case (assessing token risk) with both single and batch modes, leaving no obvious gaps for the intended purpose.