list_companiesList the public companies Ticker Scout covers, with the fiscal period of the latest data and the predicted date of the next SEC filing. Call this first when you do not know whether a company is covered, or to turn a company name into a ticker.
| Name | Required | Description | Default |
|---|---|---|---|
| query | No | Optional filter matched against ticker and company name, case-insensitive. |
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
Annotations already declare readOnlyHint, so the description doesn't need to cover safety. It adds behavioral context about what the list includes (covered companies) and the metadata shown (fiscal period, predicted next filing date). No contradictions 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?
Two concise sentences, front-loaded with action and list contents, followed by usage guidance. No wasted words, perfectly sized for its purpose.
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?
Given the tool's simplicity (one optional parameter, no output schema), the description covers purpose, output contents, and when to invoke. Annotations support the safety profile, making this complete for the tool's complexity.
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 query parameter. The description adds value by explaining the real-world use of the filter: to determine coverage or resolve a company name to a ticker, enriching the purpose beyond the basic schema text.
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 uses specific action 'List' with resource 'public companies Ticker Scout covers', and specifies the output contents (fiscal period, predicted SEC filing date). It clearly differentiates from sibling get_* tools by positioning itself as the discovery/listing tool.
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?
Explicitly states 'Call this first when you do not know whether a company is covered, or to turn a company name into a ticker.' This provides clear when-to-use guidance and implicitly contrasts with alternative family-specific tools.
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.
Each tool targets a distinct concern: company identity, event digests, full financials, headline figures, narrative text, and company enumeration. Even the potentially overlapping get_financials and get_key_figures are explicitly differentiated by scope and purpose.
All tool names follow a consistent snake_case verb_noun pattern: get_company, get_events, get_financials, get_key_figures, get_narrative, list_companies. The single list_ verb is still predictable and consistent with the get_ verbs for enumerating a collection.
Six tools is a well-scoped size for a SEC-filing and company-data server. Each tool covers a meaningful slice of the domain without redundancy or unnecessary surface area.
The tool set covers the full read-only workflow: discover coverage, get company identity, retrieve summary or detailed financials, access qualitative narrative, and review recent material events. No obvious dead ends remain for fundamental SEC-based research.