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jakobautomation

agentsvc-mcp

token_precheck

Checks up to five token addresses for honeypot or rug risk before a trade, returning ALLOW, CAUTION or BLOCK with grade, score and reason for each.

Instructions

Token Pre-Trade Check — Honeypot and rug check before a trade, built for trading bots that check every token in a loop: up to 5 token addresses per call (Base, Ethereum, BSC, Arbitrum, Polygon, Avalanche, Optimism or Solana), one decision each: ALLOW, CAUTION or BLOCK, with grade A to F, score, honeypot flag and the main reason. Based on our own on-chain checks (contract code, pools, real buy and sell simulation). Results are cached for 10 minutes, so repeat checks answer in milliseconds. Accepts token, tokenAddress or tokens. Cheaper per token than any single-token verdict. Cost: $0.004 USDC per call. Latency p99: 8000ms.

Input Schema

NameRequiredDescriptionDefault
chainNobase, ethereum, bsc, arbitrum, polygon, avalanche, optimism or solana. Default base
tokenNoSingle address (alternative to tokens)
tokensNoUp to 5 addresses, strings or {token, chain}
tokenAddressNoAlias of token

Schema Changelog

Changes observed during successful MCP inspections.

  1. First observedv1.0.0

TDQS

A4.1/5.0
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 well: it discloses the methodology (own on-chain contract/pool checks plus real buy/sell simulation), 10-minute result caching, cost ($0.004 USDC per call) and p99 latency (8000ms). These are exactly the operational traits an agent needs before invoking a paid, potentially slow call.

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-loaded with the core purpose and scope in the first clause, then progressively less critical operational detail. It is dense and long, and the 'Cheaper per token than any single-token verdict' line is promotional rather than actionable, but nothing is buried badly.

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 semantics and behavior — and it does, covering return values, chains, batch limit, cost, latency and caching. An agent has everything needed to decide and call 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 description coverage is 100%, so the schema already documents chain, token, tokenAddress and tokens. The description adds only a restatement of the three input aliases and the supported chain list, both of which are already in the schema, so the baseline 3 applies.

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

Purpose4/5

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

States a specific verb and resource — a honeypot/rug pre-trade check on token addresses — and spells out the output vocabulary (ALLOW/CAUTION/BLOCK, grade A–F, score, honeypot flag). It differentiates itself from the single-token verdict family with 'up to 5 token addresses per call' and 'Cheaper per token than any single-token verdict', though it never names the specific sibling tools.

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?

Gives clear usage context: 'before a trade' and 'built for trading bots that check every token in a loop.' That is enough for an agent to know when to reach for it, but there are no explicit exclusions or named alternatives (e.g. when to prefer token_security or token_verdict for a single deep check).

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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