TickerFacts
Server Details
SEC EDGAR fundamentals as agent tools: look up or screen US public companies. Free, no key, CC0.
- Status
- Healthy
- Last Tested
- Transport
- Streamable HTTP
- URL
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Tool Definition Quality
Average 4.2/5 across 2 of 2 tools scored.
The two tools have completely distinct purposes: one retrieves detailed fundamentals for a single company, and the other screens companies based on filters. There is no ambiguity or overlap.
Both tool names follow the consistent verb_noun pattern: get_fundamentals and screen_companies. The naming is clear and predictable.
With only two tools, the server feels thin for a financial data domain. However, the tools are broad and powerful, covering both single-company lookup and multi-company screening, so the minimal count is borderline acceptable.
The tools cover the core functionality of retrieving fundamentals and screening companies. Minor gaps exist, such as no direct access to individual financial statements (e.g., income statement, balance sheet), but the fundamentals tool returns a wide range of metrics, reducing the need for separate tools.
Available Tools
2 toolsget_fundamentalsAInspect
SEC-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 |
Tool Definition Quality
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
No annotations are provided, so the description carries the full burden. It discloses the data source (SEC EDGAR) and mentions that price-based valuation is included only when available. For a read-only tool, this is transparent, though it could mention data freshness or any rate limits.
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 sentences, no filler. The first sentence immediately conveys the tool's purpose and data types; the second provides source context. Every word is necessary and informative.
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 (1 required param, no output schema), the description adequately covers what it does and the data source. It could explicitly state that data is annual and limited to US companies (though implied by 'US public company'). Minor gap for an otherwise complete description.
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% (ticker described as 'US ticker symbol, e.g. AAPL'). The description adds context about the data returned but not additional parameter-specific details beyond the schema. 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 tool retrieves SEC-derived annual fundamentals (revenue, net income, assets, margins, plus price-based valuation) for a US public company by ticker. This specific verb+resource combination distinguishes it from the sibling tool 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?
The description implies when to use (need annual fundamentals for a specific US public company) and data source (SEC EDGAR). It does not explicitly mention when not to use or alternatives, but the sibling tool name screen_companies suggests a different purpose, providing some implicit guidance.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
screen_companiesAInspect
Find 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 |
Tool Definition Quality
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
With no annotations, the description must carry the full behavioral burden. It mentions the data source (SEC EDGAR) and that it returns a ranked list with key figures. However, it omits details about sorting defaults, pagination, rate limits, and what happens when no matches are found. While not misleading, it leaves gaps.
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, front-loaded with the main action and filters. Every word serves a purpose, no redundancy. It efficiently conveys the tool's capability without unnecessary detail.
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 adequately explains the tool's scope and filter options. It clarifies the data source and output type (ranked list). It could specify the output format more precisely, but it is sufficient for most screening use cases.
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 baseline is 3. The description adds value by grouping parameters conceptually (e.g., 'minimum revenue, margins, ROE, growth; sector; profitable-only') and stating that data traces to SEC EDGAR, providing context beyond individual parameter descriptions.
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 tool screens US public companies based on financial fundamentals from SEC EDGAR filings. The verb 'Find' and the resource 'US public companies' are specific, and the listing of filters makes the purpose unambiguous.
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 indicates when to use the tool (to find companies matching financial criteria). However, it does not explicitly contrast with the sibling tool 'get_fundamentals', leaving the agent to infer that this is for screening multiple companies rather than retrieving details for a single company.
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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