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Valuein — SEC EDGAR Fundamentals & Smart-Money Data

by io.github.valuein

Point-in-Time Valuation Ratios

get_pit_valuation_ratios
Read-onlyIdempotent

THE TOOL FOR CURRENT VALUATION MULTIPLES. Omit as_of_date and it returns TODAY'S P/E, P/S, P/B, EV/EBITDA, EV/Revenue and FCF yield, computed from the latest EOD close and the latest TTM financials. Use it for any "what is X's P/E " / "how is X valued right now" question — never derive a multiple yourself by dividing a price by an earnings figure; that is exactly the arithmetic the provenance contract forbids. Pass as_of_date to get the same snapshot on a specific historical date — zero look-ahead bias (the 'Compustat + CRSP merge' pattern). The EOD close is sourced from stock_price_daily.parquet at as_of_date (or the nearest prior trading day), and all financial figures come from SEC filings with accepted_at ≤ as_of_date so no future information is used. TTM financials are computed by summing the four most recent standalone-quarter values (or using the most recent FY filing when no quarterly series is available). Returns: price snapshot (close, price_date, is_exact_date_match), TTM P&L (revenue, gross_profit, operating_income, EBITDA, net_income, OCF, CapEx, FCF), balance sheet snapshot (shares, cash, debt, book equity), derived market values (market_cap, enterprise_value), valuation multiples (P/E, P/S, P/B, EV/EBITDA, EV/Revenue, FCF yield %), and TTM margins (gross, operating, net). Use for: historical valuation screens, backtesting entry-point multiples, forensic audit of peak / trough valuations, comparing a company's current multiples to its own history. Coverage follows your plan tier: full = all companies & full history, pro = all companies & last 15 years, sp500 = S&P 500 only, sample = S&P 500 & last 5 years. Available on all plans.

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
tickerYesStock ticker symbol, e.g. AAPL, MSFT
as_of_dateYesThe historical date for the valuation snapshot (YYYY-MM-DD). The EOD close on the nearest prior trading day will be used. All financials are PIT-filtered to filings accepted on or before this date. Use a date in the recent past (within the last year) to get current-ish multiples; use any historical date back to 1993 (subject to your plan's history window) to get the multiples as they would have been observable on that date.

Output Schema

TableJSON Schema
NameRequiredDescriptionDefault
cikYes
cashYes
noteYes
_metaYesProvenance envelope — data lineage for every MCP response
closeYes
tickerYes
ttm_fcfYes
ttm_ocfYes
currencyYes
net_debtYes
pb_ratioYes
pe_ratioYes
ps_ratioYes
ev_ebitdaYes
ttm_capexYes
as_of_dateYes
ev_revenueYes
market_capYes
price_dateYes
total_debtYes
ttm_ebitdaYes
book_equityYes
ttm_revenueYes
company_nameYes
fcf_yield_pctYes
net_margin_pctYes
shares_dilutedYes
ttm_net_incomeYes
ttm_period_endYes
enterprise_valueYes
gross_margin_pctYes
ttm_gross_profitYes
is_exact_date_matchYes
operating_margin_pctYes
ttm_operating_incomeYes
financials_accepted_atYes

TDQS

A4.1/5.0
Behavior5/5

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

Annotations indicate safe, read-only, idempotent behavior. Description adds rich detail: data source (stock_price_daily.parquet), financials from SEC filings with accepted_at ≤ as_of_date, TTM computation method, return structure with 9 categories, coverage tiers. No contradiction 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?

Well-structured with bold opening, subsequent detail, use cases, and coverage. Slightly verbose but every section adds value. Could be tightened.

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 tool complexity (2 params, output schema present, annotations), the description covers data origin, computation, return schema, use cases, and plan-specific limitations. Fully equips an agent to decide when and how to invoke.

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

Parameters1/5

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

Description states 'Omit as_of_date and it returns TODAY'S' implying it is optional, but input schema marks as_of_date as required. This direct contradiction misleads the agent into calling the tool without a required parameter, causing failure. The description adds incorrect meaning beyond the schema.

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 returns point-in-time valuation ratios (P/E, P/S, etc.) for a ticker, emphasizes it avoids look-ahead bias, and explicitly warns against manual derivation. It distinguishes from general financial ratio tools by its point-in-time nature and forbidden manual computation.

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?

Provides clear when-to-use guidance ('what is X's PE?'), and lists concrete use cases (historical screens, backtesting, forensic audit). Lacks explicit when-not-to-use or direct sibling comparison, but implicitly excludes manual derivation.

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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