SEC EDGAR fundamentals as agent tools: look up or screen US public companies. Free, no key, CC0.
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Average 4.1/5 across 2 of 2 tools scored.
The two tools have clearly distinct purposes: one retrieves fundamentals for a specific ticker, the other screens companies based on filters. No overlap or ambiguity.
Both tool names follow a consistent verb_noun pattern (get_fundamentals, screen_companies), making them predictable and easy to understand.
With only 2 tools, the server feels minimal. While it covers the basic use cases, additional tools (e.g., for historical data, financial statements) would provide more depth.
The server covers retrieving fundamentals for a single company and screening multiple companies, which are the core read operations. However, it lacks tools for historical comparisons, specific financial statements, or data export.
get_fundamentalsAInspectSEC-derived annual fundamentals (revenue, net income, assets, margins; plus price-based valuation when available) for a US public company, by ticker symbol. Data traces to SEC EDGAR filings.
| Name | Required | Description | Default |
|---|---|---|---|
| ticker | Yes | US ticker symbol, e.g. AAPL |
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
No annotations, so description carries burden. Discloses data source and type (annual, from SEC). Does not discuss edge cases (invalid ticker), rate limits, or destructive potential (none), but adequate for a read-only retrieval tool.
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 core purpose and data source. No wasted words.
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 one parameter and no output schema, description provides essential context (data source, content, scope). Could mention that data is annual only, but overall complete for a simple retrieval tool.
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 covers 100% of the single parameter ('ticker'). Description adds context ('US public company', 'by ticker symbol') clarifying usage, exceeding mere repetition.
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?
Clearly states it retrieves SEC-derived annual fundamentals (revenue, net income, etc.) for a US public company by ticker. Differentiates from sibling screen_companies.
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 use case (fundamental data for specific company) and data source (SEC EDGAR), but lacks explicit when-not-to-use or alternative guidance beyond sibling name.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
screen_companiesAInspectFind US public companies whose latest-fiscal-year fundamentals match filters (minimum revenue, margins, ROE, growth; sector; profitable-only), sorted and limited. Returns a ranked list of companies with their key figures. Data traces to SEC EDGAR filings.
| Name | Required | Description | Default |
|---|---|---|---|
| q | No | Match ticker prefix or company-name substring | |
| sort | No | Sort column; prefix with - for descending. e.g. -revenue (default), net_margin, -return_on_equity, revenue_growth | |
| limit | No | Max results, 1-100 (default 25) | |
| sector | No | One of: Agriculture, Mining, Construction, Manufacturing, Transportation & Utilities, Wholesale Trade, Retail Trade, Finance & Real Estate, Services, Public Administration | |
| roe_min | No | Minimum return on equity, percent | |
| profitable | No | Only companies with positive net income | |
| revenue_min | No | Minimum annual revenue, USD (e.g. 1000000000 for $1B) | |
| net_income_min | No | Minimum annual net income, USD | |
| net_margin_min | No | Minimum net margin, percent | |
| gross_margin_min | No | Minimum gross margin, percent (e.g. 40) | |
| total_assets_min | No | Minimum total assets, USD | |
| debt_to_equity_max | No | Maximum debt-to-equity ratio | |
| revenue_growth_min | No | Minimum year-over-year revenue growth, percent | |
| operating_margin_min | No | Minimum operating margin, percent |
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
With no annotations, the description carries full burden. It discloses data source (SEC EDGAR) and that it uses latest-fiscal-year data, but does not detail output structure or any potential side effects. Some behavioral traits are missing.
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 that efficiently convey purpose, filters, output, and data source with no unnecessary words.
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 14 parameters and no output schema, the description should provide more detail on return fields and behavior. It states 'ranked list of companies with their key figures' but does not specify which figures, leaving ambiguity.
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?
All 14 parameters have descriptions in the schema (100% coverage), so the description adds minimal extra meaning beyond summarizing filter categories. 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 clearly states the verb (screen/find) and resource (US public companies), and distinguishes from the sibling tool 'get_fundamentals' by focusing on filtering across companies rather than retrieving data for a single company.
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 implicitly suggests usage for filtering companies by fundamentals, and the sibling tool name provides context, but it lacks explicit guidance on when to choose this tool over alternatives.
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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