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Valuein — SEC EDGAR Fundamentals & Smart-Money Data

by io.github.valuein

Compute LBO Returns (IRR + MOIC)

compute_lbo
Read-onlyIdempotent

Leveraged buyout returns analysis: caller provides entry/exit multiples, leverage, and a hold period; the tool builds a Day-1 pro-forma opening balance sheet from the deal's own sources & uses (cash-free, debt-free convention — entry_debt = leverage_multiple x EBITDA, sponsor_equity = entry_enterprise_value + minimum_cash - entry_debt), then runs it through the same linked three-statement engine as project_three_statement (100% FCF-to-debt-paydown sweep by default). Returns MOIC and IRR (solved by bounded bisection over the sponsor's cash flow stream — interim dividends if any, plus exit equity proceeds). EBITDA is PROXIED by operating income (no separate D&A concept exists in the dataset) unless entry_ebitda_override is supplied — see result.entry_ebitda_is_proxy. result.irr.converged:false means no root was found (e.g. a total wipeout) — never a fabricated rate. Every simplification is listed in result.caveats[]. Tier: sp500+.

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
tickerYesStock ticker symbol of the LBO target, e.g. AAPL.
tax_rateNoEffective tax rate on positive pretax income. Default 0.21.
as_of_dateNoPoint-in-time cutoff (YYYY-MM-DD) for the seed period. Omit to use the latest knowable annual period.
minimum_cashNoMinimum operating cash left on the pro-forma opening balance sheet. Default 0.
exit_multipleNoEV/EBITDA multiple assumed at exit. Defaults to entry_multiple (no multiple expansion/contraction) when omitted.
cash_sweep_pctNoFraction (0-1) of each year's FCF swept to debt paydown. Default 1.0 (standard LBO — 100% sweep).
entry_multipleYesEV/EBITDA multiple paid at entry (e.g. 10 = 10x).
hold_period_yearsNoHold period in years (1-10). Defaults to 5.
leverage_multipleYesDebt/EBITDA raised at entry (e.g. 5 = 5x leverage).
dividend_payout_pctNoFraction (0-1) of net income distributed to the sponsor each year (dividend recap style). Default 0 — most LBOs return capital only at exit.
revenue_growth_rateYesFlat annual revenue growth rate applied every year of the hold (e.g. 0.05 = 5%/yr).
entry_ebitda_overrideNoOverride the EBITDA figure used for both entry and exit multiples. Without this, EBITDA is proxied by operating income.
interest_rate_on_debtNoAnnual interest rate on beginning-of-period LBO debt. Default 0.08 (leveraged debt typically prices above IG).

Output Schema

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

TDQS

A4.7/5.0
Behavior5/5

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

Annotations indicate readOnly, idempotent, and non-destructive behavior. The description adds significant behavioral context: EBITDA proxied by operating income, FCF-to-debt paydown sweep, IRR solving method, convergence flag, and caveats array. No contradictions.

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 detailed but every sentence adds value. It is front-loaded with the main purpose and then explains key behaviors. It could be slightly more concise, but the richness of information justifies the length.

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 (13 parameters, linked three-statement model, multiple caveats), the description thoroughly covers inputs, outputs, assumptions (e.g., EBITDA proxy), and edge cases (convergence flag). The presence of an output schema further enhances completeness.

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?

The input schema has 100% description coverage. The tool description adds critical meaning beyond the schema by explaining how parameters like leverage_multiple, entry_multiple, and minimum_cash are used in formulas (e.g., entry_debt = leverage_multiple * EBITDA, sponsor_equity = entry_enterprise_value + minimum_cash - entry_debt).

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 LBO returns (IRR and MOIC) and provides a detailed explanation of the analysis. It distinguishes itself from sibling tools like project_three_statement by referencing the same engine but with different focus.

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?

The description explains the inputs needed and the workflow (pro-forma balance sheet, three-statement projection). It implicitly sets expectations for when to use this tool over similar ones like project_three_statement, but does not explicitly mention when not to use it.

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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