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Valuein — SEC EDGAR Fundamentals & Smart-Money Data

by io.github.valuein

Project Linked Three-Statement Model

project_three_statement
Read-onlyIdempotent

Linked forward Income Statement / Balance Sheet / Cash Flow projection, seeded from the company's latest historical annual period. The balance sheet ties out (assets == liabilities + equity) EVERY projected year by algebraic construction — each year's tie_out_ok field is a live correctness check, not decoration. Interest is computed on beginning-of-period debt balances (no circular cash-sweep/revolver solve — deterministic by design). Gross margin, operating margin, and the combined D&A + working-capital adjustment are held at the seed period's ratio-of-revenue unless overridden; interest_rate_on_debt and tax_rate are ASSUMPTIONS (no historical InterestExpense concept exists in the dataset). Every simplification is listed in the response caveats[] — read them before presenting this as a precise forecast. Returns a fcf_stream usable directly as compute_dcf's fcf_source:"three_statement" input. Tier: sp500+.

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
yearsNoProjection horizon in years (1-15). Defaults to 5.
tickerYesStock ticker symbol, e.g. AAPL, MSFT, BRK.B.
tax_rateNoEffective tax rate on positive pretax income. Default 0.21 (US statutory).
as_of_dateNoPoint-in-time cutoff (YYYY-MM-DD) for the seed period. Omit to use the latest knowable annual period.
cash_sweep_pctNoFraction (0-1) of each year's free cash flow swept to debt paydown. Default 0 (going-concern; use ~1.0 for an LBO-style paydown).
dividend_payout_pctNoFraction (0-1) of net income paid out as dividends each year. Default 0.
new_debt_draw_year1NoNew debt drawn at year 1 only (absolute USD) — e.g. acquisition financing. Default 0.
revenue_growth_rateYesFlat annual revenue growth rate applied every year (e.g. 0.08 = 8%/yr).
interest_rate_on_debtNoAnnual interest rate on beginning-of-period debt. Assumption — default 0.06.
new_equity_draw_year1NoNew equity contributed at year 1 only (absolute USD) — hits cash + equity symmetrically. Default 0.
gross_margin_pct_overrideNoOverride the seed period's gross margin (held flat across all years). Leave unset to use the historical ratio.
capex_pct_of_revenue_overrideNoOverride the seed period's capex-as-%-of-revenue ratio. Leave unset to use the historical ratio.
operating_margin_pct_overrideNoOverride the seed period's operating margin. Leave unset to use the historical ratio.

Output Schema

TableJSON Schema
NameRequiredDescriptionDefault
_metaYesProvenance envelope — data lineage for every MCP response
resultYes
tickerYes
seed_period_endYes

TDQS

A4.4/5.0
Behavior5/5

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

The description adds significant behavioral context beyond annotations: explains that balance sheet ties out algebraically, interest is computed on beginning debt without circularity, margins are held flat unless overridden, and simplifications are listed in caveats. This enriches the read-only and idempotent annotations.

Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.

Conciseness3/5

Is the description appropriately sized, front-loaded, and free of redundancy?

The description is a single paragraph with several sentences covering many details. It is front-loaded with the main purpose but could benefit from structuring into bullet points for readability. Content is valuable but dense.

Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.

Completeness4/5

Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?

Given the presence of an output schema and annotations, the description covers behavioral details, assumptions, and constraints. It mentions the tier and caveats. It is fairly complete but could further clarify the seed period selection.

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. The description adds value by explaining how parameters like interest_rate_on_debt and tax_rate are assumptions, and that revenue_growth_rate is flat annually. This context aids parameter understanding.

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 the tool projects Income Statement, Balance Sheet, and Cash Flow. It specifies that it is seeded from the latest historical annual period and includes features like balance sheet tie-out and deterministic interest. This distinguishes it from sibling tools like compute_dcf or compute_lbo.

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?

It explicitly says the output fcf_stream is usable as input for compute_dcf's three_statement source, guiding when to use this tool. However, it does not explicitly state when not to use it or compare to alternatives beyond that.

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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