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TickerFacts

by com.tickerfacts
OverviewDiscussionsAdmin

get_fundamentals

SEC-derived annual observations with exact dates, units and source filings for a US public company, by ticker symbol. Includes coverage gaps, metric definitions, requested/canonical identity, dataset SHA-256 and research links. Empty annual history is unavailable, not zero; current-CIK history is not reconciled across reorganizations. Data traces to SEC EDGAR filings. For research and education only; not financial advice or a recommendation to buy, sell or hold.

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
tickerYesUS ticker symbol, e.g. AAPL
metricsNoComma-separated canonical metric keys, e.g. revenue,operating_income,capex; omitted means all curated metrics.
periodsNoNewest annual period groups to return; omitted means all available.

Schema Changelog

Changes observed during successful MCP inspections.

  1. Changed3 schema fields changed
    • addedInput schema / additionalProperties
      Added value: +false
    • addedInput schema / properties / metrics
      Added value: +{
+  "description": "Comma-separated canonical metric keys, e.g. revenue,operating_income,capex; omitted means all curated metrics.",
+  "type": "string"
+}
    • addedInput schema / properties / periods
      Added value: +{
+  "description": "Newest annual period groups to return; omitted means all available.",
+  "maximum": 20,
+  "minimum": 1,
+  "type": "integer"
+}
  2. First observed

TDQS

A3.9/5.0
Behavior5/5

Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?

With no annotations, the description carries the transparency burden and largely succeeds: it reveals data provenance (SEC EDGAR), the distinction between unavailable and zero values, the non-reconciliation of current-CIK history across reorganizations, and the research-only nature. This is materially more than a typical read-tool description and includes concrete data caveats.

Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.

Conciseness5/5

Is the description appropriately sized, front-loaded, and free of redundancy?

The description is dense but front-loaded: core purpose first, then contents, then caveats, provenance, and disclaimer. Every sentence earns its place; there is no padding.

Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.

Completeness5/5

Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?

Despite no output schema and no annotations, the description tells the agent what to expect (dates, units, filings, coverage gaps, definitions, hashes, links) and flags the two most dangerous misinterpretations (empty-as-zero and reorganization reconciliation). It is complete enough for correct invocation.

Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.

Parameters3/5

Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?

Schema coverage is 100% and each parameter already has a meaningful description, so the baseline applies. The description adds contextual color like 'annual' and 'by ticker symbol' but no param-specific information beyond the schema.

Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.

Purpose4/5

Does the description clearly state what the tool does and how it differs from similar tools?

The description states the tool returns SEC-derived annual observations with exact dates, units, and source filings for a specific US public company ticker. It is clear and specific, but it never explicitly contrasts itself with the sibling get_fundamentals_batch or other data tools, so differentiation is left to inference.

Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.

Usage Guidelines2/5

Does the description explain when to use this tool, when not to, or what alternatives exist?

No guidance is given on when to choose this tool over get_fundamentals_batch, get_company_evidence, or screen_companies. The closest usage signal is 'by ticker symbol', implying single-company data, but no exclusions, prerequisites, or alternative routing are stated.

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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