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PennyQuery

by io.github.carpdroid-commits
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Get company enrichment

get_company_enrichment
Read-onlyIdempotent

Return the enrichment record for one company: name, domain, industry, employee_count, description, last_updated (ISO 8601 UTC) and source. Call this when you already know the company's website domain or exact name and need firmographic details. Pass exactly one of domain (preferred; URLs like "https://www.acme.com/about" are normalized to "acme.com") or name (case-insensitive exact match). If you only have a partial name or a topic, call search_companies first. Returns error "not_found" when no record exists. Free: the enrichment table is a small sample of fictional companies on reserved .example domains, for trying the tools.

Tool call template

This tool declares no required parameters. Add optional parameters from the input schema as needed.

{
  "id": 1,
  "jsonrpc": "2.0",
  "method": "tools/call",
  "params": {
    "arguments": {},
    "name": "get_company_enrichment"
  }
}

Input Schema

NameRequiredDescriptionDefault
nameNoExact company name, e.g. "Acme Robotics"
domainNoCompany website domain or URL, e.g. "acme.com"

Schema Changelog

Changes observed during successful MCP inspections.

  1. First observed

TDQS

A5/5.0
Behavior5/5

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

Annotations cover readOnly/idempotent/closed-world, and the description still adds genuine value beyond them: input normalization rules (URLs collapsed to bare domain), exact-match semantics for name, the "not_found" error condition, and the caveat that the dataset is a small fictional sample. That is exactly the kind of context structured fields cannot convey.

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?

Front-loaded with what is returned, then usage conditions, then parameter rules, then edge cases. Every sentence carries non-redundant information; even the dataset note explains a practical limitation an agent would otherwise misread as real data.

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, yet the description lists the return fields, and no annotations are contradicted. For a two-parameter read tool with full schema coverage, 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.

Parameters5/5

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

Schema coverage is 100%, so the baseline is 3, but the description exceeds it by declaring the mutual-exclusivity rule ('Pass exactly one of') and a preference ordering (domain preferred), plus normalization behavior — none of which the schema encodes.

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 the specific verb (return) and resource (enrichment record for one company), enumerates the returned fields, and differentiates itself from search_companies by contrast. An agent can immediately tell what it gets and how it differs from the sibling search tool.

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 gives the trigger ('when you already know the company's website domain or exact name') and the exclusion path ('If you only have a partial name or a topic, call search_companies first'). Nothing about tool selection is left to inference.

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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