get_sharesCommon shares outstanding (latest + history) and public float for a ticker, from the SEC XBRL cover-page tags. Shares outstanding updates every 10-Q/10-K; public float is reported on the 10-K cover. The inputs for market-cap and free-float math.
| Name | Required | Description | Default |
|---|---|---|---|
| ticker | Yes | US stock ticker, e.g. 'AAPL'. | |
| history | No | Include the full reported history (default false). |
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
Annotations already cover the read-only, idempotent, non-destructive profile. The description adds useful behavioral context beyond annotations: shares outstanding update on each 10-Q/10-K, and public float comes from the 10-K cover. This helps the agent understand staleness and data provenance. It does not describe edge cases like missing float data, but the annotation coverage lowers the burden.
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?
Three sentences with no filler. The core data is stated first, then the update cadence, then the practical use case. Every sentence earns its place and the most important information is front-loaded.
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 read-only lookup with no output schema, the description covers what data is returned, its source, and its refresh pattern. It leaves minor ambiguity about whether 'history' applies to public float as well as shares outstanding, but overall it is sufficient for correct invocation.
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 coverage is 100%, so the schema already documents both ticker and history. The description adds minor context by mentioning 'latest + history' and 'public float,' but this mostly restates the schema rather than adding new parameter-level meaning. Baseline 3 is appropriate.
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 names a specific verb ('Get'), a concrete resource ('common shares outstanding' and 'public float'), and a source ('SEC XBRL cover-page tags'). This clearly identifies what the tool returns and distinguishes it from sibling tools like get_filings or get_xbrl.
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 implies usage by stating these are 'the inputs for market-cap and free-float math,' but it does not explicitly say when to prefer this tool over siblings like get_xbrl or get_filings. There are no exclusions or alternative routing conditions.
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 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.