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Read-onlyIdempotent

Search by intent everything you can call or check: tools and paid APIs (type service), offerings, agents, reviewed sites, MCP servers and packages. query in words ("weather under a cent returns JSON"); data.filters is what applied, and type, category, max_price (USD), output, network, protocol or door (agent = no human step) set one (any clears). Ranked by relevance; quality reorders close matches; each result says why, and review_url is where to read or write its reviews.

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
doorNoagent,human,wallet,key,pow,open,form,oauth,captcha,blocked,closed
typeNoservice,offering,agent,site,domain,mcp,pkg
limitNo
parseNoauto, rules or off
queryNo
cursorNo
outputNo
explainNo
networkNo
categoryNo
protocolNox402 or mpp
max_priceNoUSD

Output Schema

TableJSON Schema
NameRequiredDescriptionDefault
okYes
errorNo

Schema Changelog

Changes observed during successful MCP inspections.

  1. Changed1 schema field changed
    • addedInput schema / properties / limit / maximum
      Added value: +50
  2. Changed1 schema field changed
    • addedInput schema / properties / door
      Added value: +{
      +  "description": "agent,human,wallet,key,pow,open,form,oauth,captcha,blocked,closed",
      +  "type": "string"
      +}
  3. Changed1 schema field changed
    • addedInput schema / properties / protocol
      Added value: +{
      +  "description": "x402 or mpp",
      +  "type": "string"
      +}
  4. Added

TDQS

A3.5/5.0
Behavior4/5

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

Annotations already declare readOnly, idempotent, non-destructive and open-world, so safety is covered. The description adds genuinely useful behavior beyond that: results are ranked by relevance, quality reorders close matches, each result explains why, and review_url points to where reviews are read/written, plus it echoes applied filters as data.filters.

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

Conciseness3/5

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

It is front-loaded with what can be found, which is good, but the second half is a dense run-on of semicolon-joined clauses whose compressed notation ('set one (any clears)') is hard to parse. Information density is high but readability suffers.

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

Completeness3/5

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

Output values need not be explained since an output schema exists, and safety is covered by annotations. For a 12-parameter tool at 42% schema coverage, though, key controls like pagination (cursor, limit) and parse/explain behavior go unaddressed, leaving gaps an agent would hit when paging or tuning results.

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 only 42% and there are no declared enums, so the description carries real weight; it clarifies query form ('query in words') and the door semantics ('agent = no human step'), and lists type/category/max_price/output/network/protocol as filters. But limit, cursor, parse, explain and the odd 'set one (any clears)' filter behavior are left unexplained, so it only partially compensates.

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 (search) and an unusually broad resource set: tools/paid APIs, offerings, agents, sites, MCP servers, packages. It effectively functions as the superset of siblings like find_agents and find_offerings, but it never names those siblings or says 'use this instead of them', so differentiation is implicit rather than explicit.

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?

Gives an example query ("weather under a cent returns JSON") and a hint that door=agent means no human step, which implies usage context. However, there is no explicit when-to-use/when-not guidance and no routing against the many sibling find_* tools, so the agent must infer scope.

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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