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MetricDuck — Financial Analysis

Raw XBRL Facts

get_xbrl_facts
Read-only

Use this when the user asks for a number the standard statements don't break out — revenue by segment, geography, product or customer, customer concentration, an industry metric (medical cost ratio, reserve replacement ratio) — or a figure exactly as filed in a 10-K / 10-Q. Searches the raw XBRL facts (~3,000 per filing, with dimensional breakdowns); for standard statements and the 250+ standard metrics, get_financials is faster.

  • Breakdowns exist only where the filer tags them (usually revenue + segment profit / Adjusted EBITDA).

  • Revenue concentration / share by customer, channel, distributor, geography or product: search concentration (as-filed ConcentrationRiskPercentage, present even when the prose is qualitative).

  • A past filing: the default is the latest filing as of TODAY; pin accession_number or fiscal_year/fiscal_period.

  • A standalone quarter from a cumulative YTD line: period_history: true (recipe on that parameter).

Sign — read before quoting a direction. <raw_data>.value is the raw as-filed value: a positive magnitude for outflow / contra-asset concepts (PaymentsTo…, capex, accumulated depreciation) but signed by construction for the IncreaseDecreaseIn… working-capital family. Where a negative value carries the negated-label role, the table shows the filed face instead. (filed −) is a per-concept hint, not a guarantee: it fires wherever the filing presents that concept negatively anywhere (e.g. TXN's tax provision, whose face reads 709). When direction is load-bearing, check the edgar fact link or use get_financials (a curated statement-sign map).

Share counts: the dei EntityCommonStockSharesOutstanding ("cover-page / current", as of the filing date) is for market cap and equity value; the us-gaap CommonStockSharesOutstanding is the balance-sheet period-end count; WeightedAverageNumberOf…SharesOutstanding is the per-period EPS average. They can differ a few % — match the Period column to your task.

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
limitNoMaximum facts to return (default 50, max 200)
searchYesSearch XBRL concepts by label or name. SPACES INSIDE A TERM ARE 'AND' — every word must appear somewhere in the fact's concept name, label, OR its dimension axis/member labels AS THE FILER WROTE THEM. Commas are OR ('goodwill,impairment'). So adding a category word to narrow a search can silently DROP the series you want: the filer may name the axis something else entirely (AutoNation tags reporting-unit goodwill on 'Goodwill Reporting Units', so 'goodwill segment' eliminates it while 'goodwill' finds it). PREFER ONE WORD and filter the results yourself; widen if a multi-word search returns suspiciously few facts. Examples: 'goodwill', 'medical cost ratio', 'goodwill,impairment', 'concentration' (revenue share by customer / channel / distributor / geography / product).
tickerYesCompany ticker symbol (e.g., 'UNH', 'AAPL'). Must be exact.
form_typeNoFiling type when auto-resolving (ignored if accession_number provided). Default: 10-K (annual). FPI filers: 20-F/40-F (annual) or 6-K (interim) — the backend auto-resolves the right form family, so the default also serves FPIs.10-K
fiscal_yearNoFiscal year to look up (e.g., 2022). If omitted, uses the latest filing. Ignored if accession_number provided.
fiscal_periodNoPin the exact period when resolving by fiscal_year (Q1/Q2/Q3/Q4/FY). WITHOUT it, fiscal_year resolves to the LATEST filing of that year — wrong for 'as of <quarter>' questions (use this to get the right quarter's balance/figure). Ignored if accession_number or period_history is set. (For a concept's value across ALL periods at once, use period_history instead.)
period_historyNoReturn the searched concept's full as-filed series ACROSS filings (every period: quarter, 6-month YTD, 9-month YTD, FY) instead of one filing's facts. Use this to de-cumulate a cumulative cash-flow / income line into a standalone quarter — e.g. Q2 cash paid for acquisitions = the 6-month YTD minus the Q1 3-month (both shown, sharing the same start date). Requires search; ignores accession_number / fiscal_year.
accession_numberNoSpecific filing accession number (from list_filings — which takes vantage_date, so it gives the filing current as of a past date). If omitted, resolves automatically from form_type + fiscal_year (latest filing as of today).

Schema Changelog

Changes observed during successful MCP inspections.

  1. Changed1 schema field changed
    • changedInput schema / properties / form_type / description
      Previous value: -"Filing type when auto-resolving (ignored if accession_number provided). Default: 10-K (annual). FPI filers: 20-F/40-F (annual) or 6-K (interim) — the backend auto-resolves the right form family, so the default also serves FPIs (#558)."New value: +"Filing type when auto-resolving (ignored if accession_number provided). Default: 10-K (annual). FPI filers: 20-F/40-F (annual) or 6-K (interim) — the backend auto-resolves the right form family, so the default also serves FPIs."
  2. First observed

TDQS

A4.9/5.0
Behavior5/5

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

Annotations already cover read-only and open-world safety, but the description adds substantial behavioral detail beyond them: sign semantics for as-filed values, caveats on filed-negative hints, concentration tagging behavior, and distinctions between dei/us-gaap share counts. This goes well beyond structured fields without contradicting annotations.

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-loads the primary use case and follows with organized bullets, but the volume of detail (sign rules, share count nuance, multiple recipes) makes it long. Most sentences carry necessary operational guidance, though there is room for trimming.

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 covers return-value nuances (raw_data.value, table sign presentation) alongside parameter and usage guidance. For a complex 8-parameter search tool, it provides everything an agent needs to call it correctly.

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

Parameters5/5

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

Schema coverage is 100%, but the description still adds operational meaning: search operator rules (spaces=AND, commas=OR, prefer one word), a period_history recipe for de-cumulating, and fiscal_period pitfalls. This is well beyond schema text.

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 ('searches') and resource ('raw XBRL facts (~3,000 per filing, with dimensional breakdowns)'), and distinguishes from sibling get_financials by contrasting scope and speed. Examples of use cases (segment revenue, industry metrics, as-filed figures) make 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.

Usage Guidelines5/5

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

Explicitly says when to use (numbers standard statements don't break out, as-filed figures) and when not (standard statements and the 250+ standard metrics use get_financials). Adds scenarios for concentration search, past filings, and standalone quarters with specific parameter recipes.

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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