get_subsidiariesThe company's subsidiaries and their jurisdictions of incorporation, parsed from Exhibit 21 of its latest 10-K. Best-effort parse of an HTML exhibit; empty for filers that list only significant subsidiaries or file no Exhibit 21.
| Name | Required | Description | Default |
|---|---|---|---|
| ticker | Yes | US stock ticker or CIK, e.g. 'AAPL'. |
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
Annotations already declare readOnlyHint, idempotentHint, and destructiveHint, covering safety. The description adds valuable behavioral context beyond annotations: it is a 'best-effort parse' and may return empty results when certain conditions apply. This helps set expectations without contradicting 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 two sentences with zero fluff. It front-loads the core purpose and then efficiently conveys the caveats. Every word earns its place.
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 simple one-parameter read-only tool with rich annotations and a clear output concept, the description is complete. It covers what the tool returns, the source, and potential empty-result scenarios. Nothing an agent needs 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.
Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?
Schema description coverage is 100% for the single 'ticker' parameter, so the schema fully documents it. The description does not add additional parameter-level detail, but none is needed given the complete 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 states a specific verb ('parsed'), resource ('company's subsidiaries'), and data source ('Exhibit 21 of its latest 10-K'). It clearly distinguishes this tool from siblings like get_filings or get_company, making its purpose unmistakable.
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 provides clear context: it is used to retrieve subsidiaries from a 10-K Exhibit 21, and even notes caveats (best-effort parse, may be empty). It does not explicitly exclude alternatives, but no sibling tool overlaps with this specific function, so the implied use case is sufficient.
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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Each tool targets a distinct SEC EDGAR resource and action: company info, filings, fundamentals, ratios, insider trades, activist stakes, fund holdings, events, and narrative sections. There is no overlap in purpose, and descriptions clarify any potential ambiguity (e.g., get_activist vs get_holdings).
All tools follow a consistent 'get_' prefix followed by a singular or plural noun (e.g., get_company, get_filings, get_ratios). The pattern is uniform and predictable, making it easy to infer behavior from the name.
Nine tools is well-scoped for a financial data server covering company profiles, regulatory filings, and derived metrics. Each tool has a clear, non-redundant role, neither feeling sparse nor bloated.
The surface covers the core EDGAR resources: company metadata, filings, financial statements, ratios, insider activity, activist filings, fund holdings, material events, and narrative sections. Minor gaps exist (e.g., direct search for specific filing types or proxy statements), but agents can accomplish most research workflows without dead ends.