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Python
Remote

honeypot_check

Simulate a buy-then-sell round trip on Base or BSC to detect honeypot tokens. Returns a clear yes-or-no on whether the token can actually be sold, plus round-trip loss percentage.

Instructions

Answer one focused question: can this token actually be sold after it is bought? Executes a real buy-then-sell round trip as a simulated transaction on Base or BSC. Use it when you only need the honeypot yes-or-no, not the full safety report. Returns JSON with: is_honeypot (boolean), buy_success (boolean), sell_success (boolean), and round_trip_loss_percent (the tax or slippage lost across the round trip). Read-only: no wallet needed. Paid per call in USDC on Base via x402.

Tool call template

Replace each placeholder with a value matching the input schema. Only required parameters are included; add optional parameters as needed.

{
  "id": 1,
  "jsonrpc": "2.0",
  "method": "tools/call",
  "params": {
    "arguments": {
      "address": "<address>"
    },
    "name": "honeypot_check"
  }
}

Input Schema

NameRequiredDescriptionDefault
chainNoWhich chain the token lives on: 'base' for Base mainnet (the default) or 'bsc' for BNB Smart Chain. It must match the network the address is deployed on.base
addressYesThe ERC-20 token contract address to evaluate: a 42-character hex string starting with 0x (for example 0x4200000000000000000000000000000000000006). This is the token you are about to buy, approve, or receive.

Schema Changelog

Changes observed during successful MCP inspections.

  1. First observedv1.0.0

TDQS

A4.7/5.0
Behavior5/5

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

The description discloses that it executes a real transaction as a simulation ('simulated transaction') and is read-only ('Read-only: no wallet needed'), which is critical given no annotations. It also details the output fields (is_honeypot, buy_success, sell_success, round_trip_loss_percent) and the pricing model (paid per call in USDC on Base via x402), going beyond annotations to provide complete behavioral context.

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 concise and front-loaded: it starts with the core purpose, then details the method and output. Every sentence adds value—the use case, the return format, the read-only and payment info. No fluff or repetition.

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?

Given the tool's simplicity (2 params, no output schema, no annotations), the description covers all needed aspects: what it does, how it works, what to expect back, and usage constraints. It is complete for an agent to invoke correctly.

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?

Schema coverage is 100%, so both parameters are already described in the schema. The description does not add extra detail beyond the schema, but it references the address as 'the token you are about to buy, approve, or receive', which provides slight context. Baseline 3 is appropriate.

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 clearly states the tool's purpose: answering whether a token can be sold after being bought, using a simulated buy-sell round trip. It names the specific resource (token address), the action (real buy-then-sell round trip), and the supported chains (Base or BSC). This distinguishes it from siblings like token_safety and contract_risk, which likely provide broader analyses.

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

Usage Guidelines5/5

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

Explicitly states when to use: 'when you only need the honeypot yes-or-no, not the full safety report.' This implies alternatives (full safety report) and provides a clear exclusion criterion. It also notes the paid nature and read-only status, setting expectations for usage.

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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