Automaton Onchain Tools

bsc_honeypot_check

Use when: can I sell this BNB Smart Chain token, and what is the buy/sell tax? Can you actually sell this BNB Smart Chain token? Simulates a small buy and a sell back into its deepest WBNB pool (PancakeSwap V2/V3) with an eth_call, so nothing trades. Returns sellable yes/no, buy tax %, sell tax %, the pool used, a verdict (sellable, taxed, high tax, honeypot) and coverage notes. No pool, not a token or an inconclusive simulation is not charged. Costs $0.01.

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": {
      "token": "<token>"
    },
    "name": "bsc_honeypot_check"
  }
}

Input Schema

Name	Required	Description	Default
`token`	Yes

Schema Changelog

Changes observed during successful MCP inspections.

AddedNov 3, 2026

TDQS

A4.4/5.0

Behavior5/5

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

With no annotations, the description carries the full burden and does so: it discloses that execution is an eth_call simulation so nothing actually trades, describes the routing (deepest WBNB pool, PancakeSwap V2/V3), and lists the verdict vocabulary. It also states billing behavior, noting that no-pool, non-token and inconclusive cases are not charged, plus a $0.01 price.

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

Conciseness4/5

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

Front-loads the triggering question, then covers mechanism, return values, billing, and price in a compact block. The first two sentences restate the same question ('can I sell this token' / 'Can you actually sell this token?'), which is minor duplication.

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?

No output schema and no annotations exist, so the description must supply both the return contract and the safety profile, and it does: enumerated return fields, verdict categories, coverage notes, simulation safety, and charge exceptions. An agent has everything needed to call it and interpret the result.

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 provides a regex pattern but zero description coverage for the single `token` parameter. The description implies the input is a BSC token ('this BNB Smart Chain token') but never explicitly says to pass the token contract address, leaving some ambiguity about whether a symbol or address is expected.

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?

States a specific verb and resource: simulates a small buy and a sell back into the deepest WBNB pool on PancakeSwap V2/V3 to determine sellability and tax. The chain (BNB Smart Chain) and the honeypot/tax scope clearly separate it from siblings like base_honeypot_check or bsc_contract_risk.

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?

Opens with an explicit 'Use when' framing tied to the user questions it answers ('can I sell this token', 'what is the buy/sell tax'). It gives no exclusions or named alternatives, so an agent must infer when bsc_contract_risk or token_deep_scan would be the better call.

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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