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MCPFax Public-Data Utility API

SEC EDGAR company facts

v1_sec_company_facts
Read-onlyIdempotent

SEC EDGAR company facts: Company profile + recent filings; or XBRL financial facts for a concept. Source: SEC EDGAR. $0.01 per call · GET /v1/sec-company-facts

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
cikNoSEC CIK. Example: '320193'.
tickerNoTicker (or use cik). Example: 'AAPL'.
conceptNoXBRL concept for financials. Example: 'Revenues'.
taxonomyNoXBRL taxonomy (default us-gaap). Example: 'us-gaap'.

Schema Changelog

Changes observed during successful MCP inspections.

  1. Changed4 schema fields changed
    • addedInput schema / properties / cik / examples
      Added value: +[
      +  "320193"
      +]
    • addedInput schema / properties / concept / examples
      Added value: +[
      +  "Revenues"
      +]
    • addedInput schema / properties / taxonomy / examples
      Added value: +[
      +  "us-gaap"
      +]
    • addedInput schema / properties / ticker / examples
      Added value: +[
      +  "AAPL"
      +]
  2. First observed

TDQS

B3.4/5.0
Behavior4/5

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

The annotations already establish read-only, idempotent, open-world, non-destructive behavior. The description adds useful context beyond those: the data source, the per-call cost, the HTTP endpoint, and the fact that the tool has two distinct output modes depending on whether a XBRL concept is supplied.

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

Conciseness4/5

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

The description is compact and packed with relevant details: output modes, source, cost, and endpoint. Minor redundancy exists because 'SEC EDGAR' appears in both name/title and source, and the endpoint mirrors the tool name, but overall it is efficiently structured.

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?

With no output schema, the description does convey the high-level output categories. However, it does not specify how the two modes are selected, whether cik or ticker is required, or what happens when neither is supplied. This leaves room for an agent to mis-invoke the tool despite the 100% schema coverage.

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 100%, so the schema already documents all four parameters. The description adds only general mentions of 'concept' and 'XBRL taxonomy', which do not meaningfully clarify parameter usage beyond the existing property descriptions.

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?

The description clearly names the resource (SEC EDGAR) and specifies what it returns: company profile plus recent filings, or XBRL financial facts for a concept. It lacks an explicit verb like 'retrieve' or 'fetch', and does not contrast itself with similar data tools, so it misses the top score.

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

Usage Guidelines2/5

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

The description implies two use cases ('profile + filings' or 'XBRL financial facts') but never says when to choose this tool over alternatives such as v1_stock_quote or v1_econ_indicator. There is no explicit 'use when' guidance or mention of exclusions.

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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