PennyQuery

Look up an SEC registrant

sec_lookup_company

Read-onlyIdempotent

Free. Resolve a stock ticker, company name or CIK to SEC EDGAR registrants (US-listed public companies). Use this first when you have a ticker or a partial name and need the CIK for get_sec_filings or get_sec_financials. Matches exact ticker, then exact name, name prefix, then name substring. Returns { count, companies: [{ cik, ticker, name }] } (up to 10); an empty list means no match. Data: SEC company_tickers.json, which covers current exchange-listed registrants only.

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": "sec_lookup_company"
  }
}

Input Schema

Name	Required	Description	Default
`query`	Yes	Ticker, company name or CIK, e.g. "AAPL", "apple", "320193"

Schema Changelog

Changes observed during successful MCP inspections.

AddedOct 25, 2026

TDQS

A4.9/5.0

Behavior5/5

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

Beyond the readOnly/idempotent annotations it discloses matching precedence (exact ticker, exact name, prefix, substring), the 10-result cap, empty-list semantics, the underlying data source (company_tickers.json), and a real limitation: only current exchange-listed registrants are covered.

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 tight sentences, front-loaded with cost ("Free."), then purpose, usage, matching behavior, and return shape; nothing is redundant with the schema.

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, and the description compensates by specifying the exact return shape, result cap, and empty-result meaning, plus data-source coverage limits — everything needed to call and interpret it.

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 the param description already gives examples, but the description adds genuine meaning by explaining the match-resolution order applied to the query, which the schema does not convey.

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 (resolve) plus the resource (SEC EDGAR registrants) and the accepted inputs (ticker, name, CIK), and explicitly distinguishes itself from get_sec_filings and get_sec_financials by naming them as downstream consumers of the CIK.

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?

"Use this first when you have a ticker or a partial name and need the CIK for get_sec_filings or get_sec_financials" gives an explicit when and names the alternatives, positioning it as the entry point in the workflow.

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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