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Deep honeypot check

get_token_verdict_deep

[Deep honeypot check: holder concentration, liquidity lock, scam token] Deep honeypot check for rug pull, token safety, holder concentration, liquidity lock, and scam token. Returns the $0.01 screen plus top-10 holder %, liquidity amount, whether LP is locked or burned, owner/mint/proxy/blacklist privileges, buy/sell tax, and contract age. CLEAR, CAUTION, AVOID, or UNKNOWN. GoPlus, honeypot.is, DexScreener. ?fresh=1 skips the 10-minute cache. $0.05 USDC. Paid: $0.05 USDC on Base, paid in-band via x402 MCP (@x402/mcp; _meta "x402/payment"). Also at GET https://micro-saas-agent.onrender.com/api/verdict-deep/{chain}/{address}.

Input Schema

NameRequiredDescriptionDefault
passNoOptional GW8 day pass token (from buy_day_pass / POST /api/pass/day); skips per-call payment
chainYes
freshNo
addressYes

Schema Changelog

Changes observed during successful MCP inspections.

  1. Added

TDQS

A3.5/5.0
Behavior4/5

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

With no annotations, the description carries the burden well: it discloses the four verdict outcomes (CLEAR/CAUTION/AVOID/UNKNOWN), upstream data sources (GoPlus, honeypot.is, DexScreener), a 10-minute cache with fresh=1 bypass, and the paid x402 mechanism on Base plus a REST fallback. Only gaps are error/unsupported-chain behavior.

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

Conciseness2/5

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

Redundant throughout: the 'Deep honeypot check' phrase appears twice, and the price is stated twice ('$0.05 USDC. Paid: $0.05 USDC on Base'), plus a bracketed tag prefix restates the title and a trailing URL and GET path are appended. The core content is front-loaded but bloated by repetition.

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

Completeness4/5

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

For a paid, multi-chain tool with no output schema, the description substitutes by enumerating return fields (holder %, liquidity amount, LP lock/burn, privileges, taxes, contract age) and verdict levels, and covers payment and caching. It is largely complete for correct invocation and interpretation.

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 only 25%, so the description should compensate. It explains 'fresh' (skips the 10-minute cache) but leaves 'chain' (23-value enum including solana) and 'address' format undocumented; pass is covered by the schema itself. Partial compensation, so a middling score.

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+resource (deep honeypot/token safety check) and enumerates what it inspects: holder concentration, liquidity lock, taxes, privileges. Saying it 'Returns the $0.01 screen plus...' positions it against the shallower get_token_verdict sibling, though it never names that tool explicitly.

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

Usage Guidelines3/5

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

Implicitly signals this is the deeper/premium check over the $0.01 screen and documents ?fresh=1 cache-skip and the $0.05 cost, but gives no explicit guidance on when to choose this rather than get_token_verdict or get_token_brief, and no when-not conditions.

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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