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Neurobyteio

AgentRisk MCP

Official

check_token_risk

Check if a Base token is safe to trade by running honeypot detection, LP-lock verification, brand impersonation checks, and live sell simulations, returning a risk score and clear should-trade decision.

Instructions

Checks whether a token on Base is safe to trade before buying or swapping. Runs honeypot detection, deployer wallet history, brand impersonation checks, on-chain LP-lock verification, and a live sell simulation across every DEX on Base (Uniswap V2/V3/V4, Aerodrome). Returns a risk score, risk level, and a clear should-trade decision with structured reasons. Requires x402 payment (0.15 USDC on Base) unless the wallet still has free trial checks remaining.

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
token_addressYesBase ERC-20 contract address (0x...)

Schema Changelog

Changes observed during successful MCP inspections. Dates show when Glama detected each change.

  1. First observedv1.0.0

TDQS

A4.5/5.0
Behavior5/5

Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?

With no annotations, the description carries the full burden. It discloses the internal checks (honeypot, deployer history, impersonation, LP-lock, live sell simulation), the return value (risk score/level/should-trade decision with structured reasons), and the cost (0.15 USDC via x402, free trials). This is comprehensive transparency beyond a typical tool description.

Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.

Conciseness5/5

Is the description appropriately sized, front-loaded, and free of redundancy?

The description is three dense sentences: purpose first, then method details, then payment terms. No filler, and the most important decision-relevant info (safe to trade, payment) is prominently placed.

Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.

Completeness5/5

Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?

For a single-parameter tool with no output schema, the description fully covers what input is needed, what operations will be performed, what result shape to expect, and the cost constraint. An agent can decide whether to use it and invoke it correctly. The only minor omission is how the x402 payment is initiated, but that is likely handled outside the tool call.

Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.

Parameters3/5

Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?

The schema already documents token_address as 'Base ERC-20 contract address (0x...)' with 100% coverage. The description reinforces the Base network context but adds no new parameter-level meaning, so it stays at the schema-coverage baseline of 3.

Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.

Purpose5/5

Does the description clearly state what the tool does and how it differs from similar tools?

The description opens with a clear verb (checks), resource (token on Base), and purpose (safe to trade before buying or swapping). It then enumerates the specific checks performed, making the scope concrete. No sibling differentiation is needed because no sibling tools exist.

Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.

Usage Guidelines4/5

Does the description explain when to use this tool, when not to, or what alternatives exist?

The description explicitly states when to use: before buying or swapping a Base token. It does not mention alternatives or exclusions, but with no sibling tools that is not a significant gap. The payment requirement also implies a usage consideration, which is useful context.

Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.

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