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Valuein — SEC EDGAR Fundamentals & Smart-Money Data

by io.github.valuein

Compute M&A Accretion/Dilution

compute_accretion_dilution
Read-onlyIdempotent

M&A accretion/dilution: the standard sell-side/banker quick-screen for whether a proposed acquisition adds to (accretive) or subtracts from (dilutive) the acquirer's EPS in the first pro-forma year. Pulls net income + shares outstanding for both companies, and each side's latest EOD close (acquirer's price converts stock consideration into new shares issued; target's price is used only to disclose the offer premium). Caller sets the consideration mix (cash_pct, cash-financed by new debt or the acquirer's balance sheet), annual run-rate synergies, and the new-debt interest rate. A SINGLE pro-forma-year bridge — NOT a multi-year merger model; synergy ramp, integration costs, and purchase-price-allocation amortization (goodwill/intangibles step-up) are not modeled (see result.caveats[]). result.accretion_dilution_pct positive = accretive, negative = dilutive. Tier: sp500+.

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
cash_pctYesFraction (0-1) of deal value paid in cash; the remainder (1 - cash_pct) is paid in acquirer stock.
tax_rateNoEffective tax rate applied to synergies and the interest drag. Default 0.21.
as_of_dateNoPoint-in-time cutoff (YYYY-MM-DD) for both companies' fundamentals + prices. Omit to use the latest knowable data.
target_tickerYesTarget's stock ticker symbol, e.g. ATVI.
acquirer_tickerYesAcquirer's stock ticker symbol, e.g. MSFT.
synergies_pretaxNoPretax annual run-rate cost/revenue synergies (USD). Default 0.
cash_financing_sourceNoWhere the cash consideration is funded from. "new_debt" (default) applies an after-tax interest drag; "balance_sheet_cash" applies none.new_debt
offer_price_per_shareYesOffer price per target share (USD).
new_debt_interest_rateNoAnnual interest rate on new acquisition debt (only used when cash_financing_source is "new_debt"). Default 0.06.
target_share_price_overrideNoOverride the target's live EOD close (used only for the disclosed premium). Leave unset to use the latest R2-derived price.
acquirer_share_price_overrideNoOverride the acquirer's live EOD close. Leave unset to use the latest R2-derived price.

Output Schema

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

TDQS

A4.6/5.0
Behavior5/5

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

The description discloses behavioral traits beyond the annotations: it pulls net income, shares outstanding, and prices; the caller sets consideration mix, synergies, and interest rate; it is NOT a multi-year merger model. It also references result.caveats for excluded items. 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?

The description is dense and front-loaded with the acronym explanation, but includes all necessary information in a single paragraph. It is efficient with no wasted words, though minor structural improvements could enhance readability.

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 11 parameters (4 required), an output schema, and high complexity, the description covers input, methodology, output, and limitations (caveats). There are no gaps for an AI agent to properly invoke the tool.

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 meaning beyond the schema, e.g., explaining that cash_pct fraction determines stock consideration, that as_of_date is a point-in-time cutoff, and that price overrides replace the latest R2-derived prices.

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 M&A accretion/dilution, a specific financial quick-screen. It uses precise terms like 'accretive' and 'dilutive' and differentiates from sibling tools such as compute_dcf and compute_lbo by noting it's a single pro-forma-year bridge.

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 it is for a sell-side/banker quick-screen, models a single year, and explicitly lists what it does NOT model (synergy ramp, integration costs, PPA amortization). However, it does not explicitly state when not to use it or name alternatives.

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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