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PennyQuery

by io.github.carpdroid-commits
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Search companies

search_companies
Read-onlyIdempotent

Find companies whose name, domain, industry or description contains the query (case-insensitive substring, not semantic). Use this to discover companies or resolve a partial name to a domain, then call get_company_enrichment for the full record. Returns { count, companies: [{ name, domain, industry }] }, best name matches first; an empty list means no matches. Default 10 results, max 25. Free.

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": {
      "query": "<query>"
    },
    "name": "search_companies"
  }
}

Input Schema

NameRequiredDescriptionDefault
limitNoMaximum results (default 10)
queryYesText to look for, e.g. "robotics" or "fintech"

Schema Changelog

Changes observed during successful MCP inspections.

  1. First observed

TDQS

A4.9/5.0
Behavior5/5

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

Annotations already declare read-only/idempotent, so the description focuses on what they don't cover: matching semantics, result ordering (best name matches first), the meaning of an empty list, the result envelope, result caps, and cost (free). That is substantive context beyond structured fields.

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

Conciseness5/5

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

Four compact sentences, front-loaded with what it does, then when to use it, then return shape and limits. No filler, and each sentence carries actionable information.

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 exists, but the description supplies the return shape, ordering, empty-result semantics, and result caps, so an agent knows exactly what to expect. Nothing needed to call it correctly is missing.

Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.

Parameters4/5

Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?

Schema coverage is 100% and documents both params, but the description adds real meaning the schema lacks: that the query is matched as a case-insensitive substring across four specific fields and is explicitly not semantic. The limit default/max is partly redundant with the schema.

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 (find companies) plus the exact fields matched (name, domain, industry, description), which distinguishes it from SEC-focused siblings and get_company_enrichment. An agent can pick it out without opening the schema.

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?

Explicitly names two use cases (discover companies, resolve a partial name to a domain) and routes the agent to get_company_enrichment for the full record. The chaining instruction removes any ambiguity about what to do next.

Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.

All PennyQuery toolsInspect
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