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Market Intelligence API (core)

Token verdict

token.verdict
Read-onlyIdempotent

Use when the user asks "is this token safe?", "is it a rug or a honeypot?", "check this CA" or "can I sell it?". Send a token contract address (Base by default; also ethereum, arbitrum, optimism, polygon, or a Solana mint), get a short verdict: LOW_RISK, CAUTION or HIGH_RISK, a 0-100 safety score, one plain-language sentence, whether a simulated sale succeeds (honeypot check), observed buy tax, liquidity, who controls the contract, the top three risks, a oneLiner to quote and a share card. Cheaper and shorter than token.risk. Use for a quick safe-to-buy answer; for every check behind it use token.risk.

Input Schema

NameRequiredDescriptionDefault
chainNoChain of the token or pool: base (default), ethereum, arbitrum, optimism or polygon (solana where listed). Not the payment networkbase
addressYes0x-prefixed ERC-20 contract, or a base58 Solana mint

Schema Changelog

Changes observed during successful MCP inspections.

  1. Added

TDQS

A4.4/5.0
Behavior4/5

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

Annotations already declare readOnly/idempotent/openWorld/non-destructive, so the safety profile is covered. With no output schema, the description usefully discloses the returned shape (verdict, score, simulated-sale honeypot check, buy tax, controller, top risks, share card) and that it is deliberately cheaper and shorter than token.risk. It does not mention auth needs or rate/latency limits, keeping it just short of a 5.

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 trigger phrases, then inputs, then outputs, then sibling routing — every sentence earns its place. It is dense and runs as one long sentence, which costs some readability, but there is no filler.

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 two-parameter tool with no output schema, the description fully carries the return-value burden, enumerating every field an agent would need to interpret or quote, plus the sibling routing. Nothing required to call or use it is missing.

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 documented, including the chain enum, the base default, and the address format. The description restates the same chain list and address format without adding syntax or edge-case meaning beyond the schema. Baseline 3 applies.

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+resource (send a token contract address, get a verdict) and enumerates the concrete outputs: LOW_RISK/CAUTION/HIGH_RISK, safety score, honeypot simulation, buy tax, liquidity. It explicitly distinguishes itself from the sibling token.risk in both scope and cost.

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?

Opens with explicit trigger phrasings ("is this token safe?", "is it a rug or a honeypot?", "check this CA") and names the alternative with the condition that selects it: "for every check behind it use token.risk." When-to-use and when-to-use-something-else are both covered.

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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