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Valuein — SEC EDGAR Fundamentals & Smart-Money Data

Financial Ratios

get_financial_ratios
Read-onlyIdempotent

Get pipeline-computed financial ratios from ratio.parquet. Served categories: profitability (margins, ROE, ROA, ROIC), liquidity (current ratio, quick ratio), leverage (D/E, interest coverage, net debt/EBITDA), efficiency (asset turnover, inventory days), per_share (EPS, BVPS, FCF/share), owner_earnings (Buffett FCF, owner yield), valuation (pe_ratio, pb_ratio, ev_ebitda, market_cap, dividend_yield), and the pipeline-emitted forensic, growth, and rank (cross-sectional *_sector_pctile) categories. NOT every category exists for every ticker — omit categories to get whatever this ticker has, or read available_categories in the CATEGORY_NOT_AVAILABLE envelope. valuation is LIVE (schema 2.18.0): price-derived multiples from EOD prices period-end-aligned — pipeline-derived, NOT strictly PIT (no accepted_at column on these rows). Includes TTM rows alongside annual; each row's is_calendar_aligned is TRUE only when period_end sits on the fiscal-year boundary (±7 days) — filter to TRUE when joining ratios to fact-table fundamentals on (entity, fiscal_year). For historical cuts use as_of_date (PIT by accepted_at when present, else by period_end — see the param). Use this instead of get_valuation_metrics when you only need ratios (no DCF wiring); use get_valuation_metrics when you also need DCF/DDM. Each ratio is a {value, unit, category, reason} entry with a response-level lineage (DerivedLineage) pointing to get_company_fundamentals / verify_fact_lineage for filing-level provenance; a null value carries a reason (e.g. INPUT_MISSING) so missing is never a real zero. Available on all plans.

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
limitNoNumber of distinct period_end dates to return (1–20). Defaults to 5. Within each period, all matching ratio_names are included.
tickerYesStock ticker symbol, e.g. AAPL, MSFT
as_of_dateNoHistorical cutoff (canonical cross-tool date param). PIT by SEC accepted_at when the ratio data carries it (latest value knowable on/before the date, zero look-ahead, _meta.pit_safe=true), else by ratio.period_end (pit_safe=false). For guaranteed accepted_at PIT use get_company_fundamentals.
categoriesNoRatio categories to include (see the enum). Omit to return every category this ticker has. `valuation` (pe_ratio, pb_ratio, ev_ebitda, market_cap, dividend_yield) is LIVE since schema 2.18.0 — price-derived, period-end-aligned, not strictly PIT. Availability is per-ticker (the envelope lists this ticker's available_categories).
fiscal_periodNoFilter to a specific fiscal period type. Use 'TTM' for trailing twelve months. Omit to return both annual (FY) and TTM rows.
period_end_beforeNoAlias of as_of_date (as_of_date preferred — the canonical name). Returns ratios with period_end on or before this date.

Output Schema

TableJSON Schema
NameRequiredDescriptionDefault
dataYes
noteYes
planYes
_metaYesProvenance envelope — data lineage for every MCP response
tickerYes
lineageNoProvenance for pipeline-derived values (ratio.parquet / factor_scores.parquet): source table + pipeline computed_at, plus a pointer to the tools that return filing-level lineage. NOT point-in-time (recomputed on each pipeline run).
periods_returnedYes

TDQS

A4.8/5.0
Behavior5/5

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

Annotations already declare readOnlyHint, idempotentHint, and destructiveHint. The description adds significant behavioral context: explains that each ratio entry contains value, unit, category, reason, and that missing values have a reason (not zero). It details that valuation is live and not strictly PIT, explains the as_of_date PIT logic, and mentions lineage provenance. No contradictions with 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?

The description is relatively long but front-loaded with the core purpose and key differentiator. Every sentence serves a purpose, but some details (e.g., is_calendar_aligned, specific join advice) could be condensed without losing clarity. Overall well-structured.

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?

Given the complexity (6 parameters, 100% schema coverage, output schema exists), the description thoroughly explains the return format (value, unit, category, reason), missing data handling, lineage provenance, and usage nuances like live vs. PIT data. It covers all necessary context for correct tool invocation.

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% with descriptions for all parameters. The description adds extra context: for categories, it explains valuation is LIVE and ticker-specific; for as_of_date, it details PIT behavior; for period_end_before, it notes it's an alias. This adds value beyond the schema, though the schema already captures the basic meaning.

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?

The description clearly states it retrieves pipeline-computed financial ratios from a specific data source and lists all served categories. It explicitly distinguishes itself from the sibling tool get_valuation_metrics by specifying when to use each, providing strong differentiation.

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?

Explicit guidance is given on when to use this tool versus get_valuation_metrics. It also advises omitting categories to get all available data and how to handle CategoryNotAvailable envelopes. Instructions for is_calendar_aligned when joining ratio results are provided, making usage clear.

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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TDQS

A4/5.0
Disambiguation5/5

Each tool has a distinct purpose with detailed descriptions that clarify differences. Overlaps like get_peer_comparables vs screen_universe are well-differentiated by scope (single company vs cross-sectional). Similarly, get_insider_sentiment vs get_smart_money_flow are clearly distinguished by data sources and methodology.

Naming Consistency5/5

All tool names follow a consistent verb_noun snake_case pattern (e.g., create_report, get_financial_ratios, delete_alert). No mixing of conventions or inconsistent verbs.

Tool Count2/5

With 69 tools, the count far exceeds the 25+ threshold for 'too many'. While the domain is broad, the sheer volume likely overwhelms agents and increases selection complexity.

Completeness4/5

The tool set covers a wide range of SEC filings, ratios, smart-money data, alerts, reports, and more. Minor gaps exist (e.g., no options or detailed debt data), but most analyst workflows are supported.