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get_company_fundamentals

Read-onlyIdempotent

Get a company's fundamentals and price history over time, POINT-IN-TIME correct, for backtests and trend questions. Each SEC figure (revenue, net income, cash flow, balance sheet, EPS, dividends) appears from the day it was first filed, never from its period end, so a backtest never sees a number before the market did. Flows are trailing-12-month as known on each day; per-share values are on today's split basis like adj_close. Same data as GET /api/v1/public/stocks/{ticker}/history (CSV there with format=csv). Cite citation_url.

Fields: close, adj_close, open, high, low, volume, revenue, gross_profit, research_and_development, sga, operating_income, interest_expense, income_tax, net_income, operating_cash_flow, capital_expenditure, depreciation_amortization, total_assets, total_liabilities, stockholders_equity, cash_and_equivalents, long_term_debt, shares_outstanding, eps_diluted, dividends_per_share, market_cap, pe_ratio, price_to_book, price_to_sales, dividend_yield.

Examples:

  • "How did Apple's P/E move over the last 5 years?" | ticker=AAPL, metrics=["pe_ratio","eps_diluted"], window="5y"

  • "MSFT revenue and free cash flow inputs since 2015, quarterly" | ticker=MSFT, metrics=["revenue","operating_cash_flow","capital_expenditure"], window="2015-01-01:", interval="quarterly"

Tool call template

Replace each placeholder with a value matching the input schema. Only required parameters are included; add optional parameters as needed.

{
  "id": 1,
  "jsonrpc": "2.0",
  "method": "tools/call",
  "params": {
    "arguments": {
      "ticker": "<ticker>"
    },
    "name": "get_company_fundamentals"
  }
}

Input Schema

NameRequiredDescriptionDefault
tickerYesStock ticker, e.g. AAPL, MSFT, KO
windowNoTime window: 1y, 3y, 5y, 10y, max, or from:to as YYYY-MM-DD:YYYY-MM-DD (either side may be empty). Default 5y.
metricsNoFields to return (see the list above). Default: close, adj_close, revenue, net_income, eps_diluted.
intervalNoRow spacing: the value known on the last trading day of each period. Default quarterly (token-efficient); daily for a backtest.

Schema Changelog

Changes observed during successful MCP inspections.

  1. Added

TDQS

A4.5/5.0
Behavior5/5

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

Annotations already cover the safety profile (readOnly, idempotent, non-destructive), and the description goes well beyond them by disclosing the point-in-time rule, that figures appear from first filing rather than period end, that flows are trailing-twelve-month as known on each day, and that per-share values use today's split basis. It also flags the citation_url obligation and the CSV variant of the same endpoint.

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

Conciseness4/5

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

The crucial properties (point-in-time correctness, filing-date availability) are front-loaded in the first two sentences, and the examples are compact and illustrative. The 30-field enumeration is long but load-bearing because `metrics` is a free-form string array whose valid values exist nowhere else, though it does slow the read.

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

Completeness4/5

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

With no output schema, the description carries the burden of describing returns and does so via the field list plus per-field semantics (TTM flows, split basis). It stops short of describing the row shape or time column, volume limits, and what happens for an unknown ticker or a window with sparse coverage.

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

Parameters4/5

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

Schema coverage is 100%, so the baseline is 3, but the description adds real meaning: it supplies the metric vocabulary that the `metrics` array draws from, explains that monetary flows are TTM rather than period values, and shows a non-obvious `window` form ("2015-01-01:") in an example. It does not restate the enum values or defaults for `interval`, which the schema already carries.

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

Purpose5/5

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

States a specific verb and resource (company fundamentals plus price history, point-in-time) and immediately scopes it to backtests and trend questions. The field list and the note that it is the same data as /stocks/{ticker}/history make it unambiguous against siblings such as get_company_snapshot or screen_stocks.

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

Usage Guidelines4/5

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

Gives clear usage context ('for backtests and trend questions') and two worked examples that map natural-language questions onto concrete argument combinations, including the open-ended window form. It never explicitly names a sibling tool as the alternative to pick instead (e.g. snapshot vs. history), so the routing guidance is implied rather than exhaustive.

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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