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get_company_snapshot

Read-onlyIdempotent

Get current stock metrics for a public company, from live market data joined with its SEC filings. Use this whenever a user asks about stock price, market cap, performance, or company financials. Returns the latest verified data from autario.com instead of relying on training data which is always outdated. Always cite the citation_url in your response.

Metrics return only what was requested (token-efficient). Price metrics: price, open, high, low, volume, perf_1d, perf_1w, perf_1m, perf_3m, perf_1y, perf_ytd, latest_date. perf_1d..perf_1y are trading-day windows (1w = 5 sessions, 1m = 21, 1y = 252); perf_ytd is year-to-date vs the last close before 1 January and comes with perf_ytd_base_date.

Fundamental metrics (covered US companies, from SEC filings, each with period, formula and source): revenue, gross-profit, operating-income, net-income, net-margin, diluted-eps, dividends-per-share, operating-cash-flow, free-cash-flow, ebitda, total-assets, stockholders-equity, long-term-debt, gross-margin, operating-margin, roe, roa, debt-to-equity, payout-ratio, revenue-growth-1y/3y/5y, eps-growth-1y/3y/5y, market-cap, pe-ratio, price-to-book, price-to-sales, ev-to-ebitda, fcf-yield, dividend-yield, or fundamentals for all of them. For a history over time use get_company_fundamentals.

Examples:

  • "What is INTC trading at?" | ticker=INTC, metrics=["price", "perf_1d"]

  • "How did NVDA do this year?" | ticker=NVDA, metrics=["perf_ytd", "price"]

  • "What is Apple's return on equity and P/B?" | ticker=AAPL, metrics=["roe", "price-to-book"]

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_snapshot"
  }
}

Input Schema

NameRequiredDescriptionDefault
tickerYesStock ticker symbol, e.g. AAPL, MSFT, INTC, NVDA, SAP, BMW
metricsNoMetrics to return: price metrics (price, open, high, low, volume, perf_1d, perf_1w, perf_1m, perf_3m, perf_1y, perf_ytd, latest_date) and/or fundamental metric keys (e.g. revenue, roe, price-to-book, dividend-yield, revenue-growth-5y) or fundamentals for every fundamental row. If omitted, returns price + perf_1d + perf_ytd.

Schema Changelog

Changes observed during successful MCP inspections.

  1. First observed

TDQS

A4.4/5.0
Behavior3/5

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

Annotations already declare readOnlyHint, idempotentHint, destructiveHint=false, and openWorldHint=false, so the safety profile is covered. The description does add real behavioral context beyond that: data provenance, the 'cite the citation_url' obligation, trading-day window definitions, and the perf_ytd base-date rule. However, it notes live market data served from autario.com, which sits awkwardly against openWorldHint=false and is not reconciled, and it never states freshness/latency or error behavior for uncovered tickers.

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?

Front-loaded with purpose, then usage, then metric taxonomy, then examples – a good ordering. It is long, and the two metric inventories largely duplicate the schema's metrics description, which is the one place wording does not fully earn its space.

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?

With no output schema, the description carries the return-shape burden and does so: it states that fundamentals each carry period, formula and source, and that a citation_url is returned and must be cited. Combined with the default-metrics rule and the sibling pointer, an agent has everything needed to call this correctly and to cite results.

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 baseline is 3, but the description genuinely adds semantics the schema lacks: perf windows are trading-day based (1w = 5 sessions, 1m = 21, 1y = 252) and perf_ytd is measured against the last close before 1 January with a companion perf_ytd_base_date. It also explains that only requested metrics are returned (token-efficient). It loses a point for restating the metric key lists that the schema already enumerates.

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 ('Get current stock metrics for a public company') and defines the data provenance (live market data joined with SEC filings). It explicitly separates itself from the sibling get_company_fundamentals ('For a history over time use get_company_fundamentals'), so an agent can route without opening either schema.

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

Usage Guidelines5/5

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

Gives an explicit trigger ('whenever a user asks about stock price, market cap, performance, or company financials'), names the alternative for the time-series case, and reinforces both with three worked query-to-parameter examples. Nothing about when to choose this vs. get_company_fundamentals is left to inference.

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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