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Valuein — SEC EDGAR Fundamentals & Smart-Money Data

by io.github.valuein

Valuation Metrics

get_valuation_metrics
Read-onlyIdempotent

Get comprehensive valuation and profitability metrics for a US public company. Returns per-period data combining computed ratios (gross_margin, operating_margin, net_margin, ROE, ROA, ROIC, debt_to_equity, FCF, FCF margin), price-derived valuation_multiples (current_price, market_cap, pe_ratio, pb_ratio, ev_ebitda, dividend_yield), and optional pre-computed DCF model inputs (WACC, fcf_base_per_share, stage1_growth_rate, terminal_growth_rate, dcf_value_per_share, ddm_value_per_share). Profitability/cash-flow/leverage fields come from fact.parquet (PIT-safe via accepted_at). valuation_multiples are LIVE (schema 2.18.0): they come from ratio.parquet's valuation category + stock_price.parquet period-end close (per-period current_price for every fiscal year), derived from EOD prices period-end-aligned. Each multiple is a {value, unit} pair (unit varies: x / USD / percent); a null value carries a null_reasons[field] PRICE_NOT_AVAILABLE code (no period-end-aligned close). DCF/DDM fields come from valuation.parquet (pipeline-computed, recomputed each run — NOT strictly PIT-safe) and are commonly null (newer tickers, transition periods, or before the valuation pipeline runs). Each null carries a null_reasons[field] code — ALWAYS check it before assuming zero (null != 0). For strict-PIT DCF, use the SDK or compute from get_company_fundamentals. Use this instead of get_financial_ratios when DCF/intrinsic value or price multiples matter; use get_financial_ratios when you only need the raw ratio table. Available on all plans.

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
limitNoMaximum number of periods to return (1–40). Defaults to 5.
periodNoFiling period granularity. Annual uses 10-K; quarterly uses 10-Q.annual
tickerYesStock ticker symbol, e.g. AAPL, MSFT
as_of_dateNoPoint-in-time date (YYYY-MM-DD). Only returns data with accepted_at on or before this date. Eliminates look-ahead bias for backtesting.
fiscal_yearNoFiscal year (YYYY). Omit to return most recent periods.

Output Schema

TableJSON Schema
NameRequiredDescriptionDefault
dataYes
_metaYesProvenance envelope — data lineage for every MCP response
periodYes
tickerYes
dcf_pitNoPresent only when as_of_date is supplied. The DCF/DDM leg comes from valuation.parquet, which is filtered by created_at (the pipeline computation timestamp), NOT the SEC accepted_at — so even with an as_of_date cut the DCF figures are BEST-EFFORT point-in-time, not strictly look-ahead-free. The profitability/cash-flow/leverage legs ARE strictly PIT-safe (fact.parquet, accepted_at).
as_of_dateYes
periods_returnedYes

TDQS

A4.7/5.0
Behavior5/5

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

Annotations already declare readOnlyHint=true and idempotentHint=true. The description adds substantial behavioral context beyond annotations: PIT-safety of fact.parquet fields, live nature of valuation_multiples, null_reasons handling (null != 0), DCF recomputation caveats, and guidance for strict-PIT DCF. This is far beyond the structured fields and is critical for correct interpretation.

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?

Though lengthy, the description is information-dense and logically structured: purpose, returns, data sources, null handling, alternatives, availability. Every sentence adds value; there is no redundancy or filler. The front-loaded purpose ensures immediate understanding.

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 tool's complexity (mixed PIT-safe and live fields, optional DCF inputs, null semantics) and rich output schema, the description is complete. It explains return value shapes, null reporting, PIT caveats, and recommends alternatives. No critical aspect is left unaddressed.

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 description coverage is 100% and each parameter already has clear documentation. The description does not add new parameter-level semantics; it focuses on return fields and data lineage. Baseline 3 is appropriate because the schema carries the burden.

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 opens with a specific verb+resource: 'Get comprehensive valuation and profitability metrics for a US public company.' It then enumerates exact returned field groups, clearly distinguishing this from sibling tools like get_financial_ratios.

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 usage guidance is provided: 'Use this *instead of* `get_financial_ratios` when DCF/intrinsic value or price multiples matter; use `get_financial_ratios` when you only need the raw ratio table.' This names the alternative and states when to prefer each. Additional context about PIT-safety and DCF availability further guides appropriate use.

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.

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