v1_sec_company_factsSEC 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
| Name | Required | Description | Default |
|---|---|---|---|
| cik | No | SEC CIK. Example: '320193'. | |
| ticker | No | Ticker (or use cik). Example: 'AAPL'. | |
| concept | No | XBRL concept for financials. Example: 'Revenues'. | |
| taxonomy | No | XBRL taxonomy (default us-gaap). Example: 'us-gaap'. |
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
The annotations already cover read-only, idempotent, and non-destructive behavior. The description adds useful behavioral context by distinguishing between the default company-profile/filings mode and the concept-specific XBRL mode, plus source and cost information. No contradiction with annotations.
Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.
Is the description appropriately sized, front-loaded, and free of redundancy?
The description is concise and front-loaded with the core functionality. The endpoint and per-call cost are extra but useful operational details; they do not bloat the text significantly.
Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.
Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?
For a read-only lookup tool with no required parameters and no output schema, the description provides sufficient context: it explains the two output modes and identifies the data source. It does not detail output formatting or edge cases, but those are not declared by the schema either and are less critical here.
Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.
Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?
The input schema has 100% parameter description coverage, so the description does not need to re-document each parameter. It does add a little semantic linkage between the two output modes and the concept/taxonomy parameters, but most parameter meaning is already in the schema.
Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.
Does the description clearly state what the tool does and how it differs from similar tools?
The description clearly states the resource (SEC EDGAR company facts) and the two distinct kinds of output: company profile plus recent filings, or XBRL financial facts for a concept. This differentiates it from sibling tools like stock quotes or economic indicators.
Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.
Does the description explain when to use this tool, when not to, or what alternatives exist?
The description gives clear context for when to use the tool: for SEC company profiles, recent filings, or XBRL concept facts. It does not explicitly name alternatives or exclusion conditions, but the use cases are specific enough to guide selection.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
Add one secure layer between your agents and this server.
Every tool targets a distinct resource or operation—geocoding, weather, DNS, VIN, stock quotes, etc.—with no meaningful overlap in purpose. Even the location- and finance-related tools are clearly separated by their descriptions.
All tools follow the same v1_<resource>[_modifier] snake_case pattern, such as v1_air_quality, v1_reverse_geocode, and v1_validate_email. Although the names are not verb-based, the convention is perfectly consistent across all 31 tools.
31 tools exceeds the 25+ threshold and creates a heavy selection burden for agents, even though the server's stated purpose is broad. Many endpoints are small single-purpose lookups that could be grouped into fewer combined tools without losing clarity.
As a general public-data utility, the set covers a wide range of common lookup categories: location, weather, finance, legal, health, business, internet, and reference data. It has minor gaps like historical financial time series or phone-number validation, but no obvious dead ends since all tools are self-contained read-only lookups.