Cross-company cohort percentiles (p10/p25/median/p75/p90) for a metric, by industry (2-digit SIC) and size bucket. Metrics: gross_margin, operating_margin, roe, rd_intensity, debt_to_equity, revenue, fcf, ... Cost: $0.05.
| Name | Required | Description | Default |
|---|---|---|---|
| fy | No | ||
| size | No | ||
| metric | Yes | ||
| industry | No |
| Name | Required | Description | Default |
|---|---|---|---|
| result | Yes |
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
Without annotations, description adds some behavioral context: percentiles computed, metrics list, cost. Lacks details on data coverage, missing values, or response structure (though output schema exists). Adequate but not rich.
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?
Concise single sentence with list and cost note. No unnecessary words, but could be more structured with parameter hints.
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 tool complexity and output schema existence, description covers core functionality but omits details on optional parameters (fy, industry format). Adequate for basic use but incomplete for advanced usage.
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 0%, so description must compensate. It only describes 'metric' (example list) and implies 'industry' (2-digit SIC) and 'size bucket'. Parameter 'fy' is not mentioned, and 'size' is vague. Insufficient to fully guide parameter usage.
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 that it provides cross-company cohort percentiles for a metric by industry and size bucket, listing example metrics. It is specific about the resource (percentiles) and grouping, but does not differentiate from sibling tools explicitly.
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?
Implies usage when industry aggregate percentiles are needed, and mentions cost ($0.05) as a constraint. However, no explicit when-to-use vs alternatives or 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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Each tool has a distinct purpose: quality scoring, valuation, raw facts, filings, financials, peer comparison, industry aggregates, insider activity, institutional ownership, market insights, screening, short pressure, and standard scores. No overlaps in functionality.
All tools use consistent snake_case naming with descriptive prefixes (e.g., company_, buffett_, market_). The pattern is predictable and helps infer tool purpose.
14 tools cover a broad range of SEC fundamentals without being overwhelming. Each tool serves a clear role, and the count is appropriate for a comprehensive financial data API.
Covers core areas: filings, financials, insider trades, institutional ownership, screening, scores, and market insights. Minor gaps (e.g., no direct analyst estimates or corporate events) but overall very well-scoped for the domain.