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Valuein — SEC EDGAR Fundamentals & Smart-Money Data

Compute Forward DCF

compute_dcf
Read-onlyIdempotent

Forward discounted-cash-flow valuation (two-stage Gordon-growth model): caller provides growth + WACC + terminal assumptions, returns per-share intrinsic value (value_per_share_cents, cents USD) + 5×5 sensitivity grid. Pulls FCF base + net debt + shares from R2; caller can override any field. Definitions (consistent with get_financial_ratios / get_capital_allocation_profile): FCF base = operating_cash_flow − capex (absolute USD); net_debt = total_debt − (cash + short-term investments). Shares resolve via a fallback chain (valuation row → fact CommonSharesOutstanding → net_income/eps_diluted), reported as result.shares_source. The pulled inputs are echoed in result.inputs_echo with their source lineage so the valuation is reproducible and traceable. A null value_per_share_cents means the model is degenerate (e.g. WACC ≤ terminal growth, or FCF base ≤ 0) or a required input was unavailable — it is NOT a zero valuation; the reason field explains. Use the returned figures exactly. Use this when you want to drive the assumptions yourself; for the pipeline's pre-computed DCF/DDM value and inputs (no assumptions needed) use get_valuation_metrics instead. Does NOT persist a report — use create_report (report_type:'reverse_dcf') for that.

fcf_source (default "trend"): "trend" compounds a single FCF base by stage1_growth_rate every year (the original behavior, unchanged). "three_statement" instead runs a full linked Income Statement / Balance Sheet / Cash Flow projection (project_three_statement's engine) and feeds its year-by-year FCF stream into the same PV math — stage1_growth_rate is then ignored (kept for echo only) because revenue growth + margins drive FCF instead of a flat compounding rate. The projection detail (including per-year tie_out_ok) is returned in three_statement_detail when used. Tier: sp500+.

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
waccNoDiscount rate. Default 0.09.
tickerYesStock ticker symbol of the company to value, e.g. AAPL, MSFT, BRK.B.
as_of_dateNoPoint-in-time cutoff (YYYY-MM-DD) for the auto-pulled inputs. Fundamentals are filtered by SEC accepted_at (strict PIT); valuation.parquet inputs are best-effort PIT (filtered by created_at, its accepted_at proxy — no SEC acceptance timestamp exists for pipeline-computed valuations). Omit to use the latest knowable inputs.
fcf_sourceNo"trend" (default): compound fcf_base by stage1_growth_rate every year (unchanged original behavior). "three_statement": derive the FCF stream from a full linked 3-statement projection instead — see the tool description for details.trend
stage1_yearsNoNumber of explicit high-growth projection years before the terminal stage (3–15). Defaults to 5.
shares_overrideNoOverride shares outstanding. Leave unset to use R2-derived.
fcf_base_overrideNoOverride the auto-pulled FCF base (USD). Leave unset to use R2-derived.
stage1_growth_rateYesStage-1 FCF growth rate (e.g. 0.12 = 12%/yr).
terminal_growth_rateNoLong-run growth. Default 0.025.
three_statement_assumptionsNoOnly used when fcf_source is "three_statement". Overrides for the underlying projection; unset fields use project_three_statement's defaults.

Output Schema

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

TDQS

A4.9/5.0
Behavior5/5

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

Annotations already provide readOnlyHint and destructiveHint; the description adds substantial beyond: null value means degenerate model not zero, detailed 'fcf_source' parameter behavior, and echoed inputs for reproducibility. 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?

Well-structured with front-loaded purpose and output, followed by parameter details and usage guidance. Slightly verbose, as some details on 'fcf_source' appear in both description and schema, but overall appropriate for the tool's complexity.

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 (10 parameters, nested objects, output schema exists), the description covers the model, inputs, outputs, edge cases, parameter behavior, and interactions with other tools. It is fully sufficient for an agent to select and invoke 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 description coverage is 100%, providing baseline of 3. The description adds significant value: explains formulas for FCF base and net debt, shares resolution fallback, and details the 'fcf_source' parameter with the 'trend' vs 'three_statement' distinction, all beyond the schema's brief descriptions.

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 computes a forward DCF valuation using a two-stage Gordon growth model and specifies the output: per-share intrinsic value and a sensitivity grid. It distinguishes from the sibling 'get_valuation_metrics' by noting that this tool lets the user drive assumptions, while the sibling provides pre-computed values.

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 advises when to use this tool ('when you want to drive the assumptions yourself') and when to use the alternative ('get_valuation_metrics'). Also clarifies that this tool does not persist a report, and directs the user to 'create_report' for that purpose.

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.