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validate_financials

Institutional validation pass over company financials: tax-rate sanity, EBITDA/EBIT bridge, growth continuity vs historical CAGR, capex ratio, working-capital stability. Returns integrity score + severity-ranked flags. $0.50/call after free trial.

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
financialsYes

TDQS

B3.4/5.0
Behavior4/5

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

No annotations are provided, so the description carries the burden of behavioral disclosure. It discloses that the tool returns an integrity score and severity-ranked flags, and mentions the cost model. It does not explicitly state whether the operation is read-only, but 'validation pass' strongly implies non-destructive behavior. However, it omits details on error handling, required financial data structure, or rate limits beyond pricing, leaving some gaps.

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 compact, using a colon-separated list to enumerate validation checks in the first sentence and a second sentence for output and pricing. It is efficiently structured with no wasted words, though the dense list could be slightly harder to parse quickly. The pricing note is extra but useful.

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

Completeness2/5

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

Given the tool's complexity (nested object parameter, no output schema, no annotations), the description is incomplete. It explains the output (integrity score + flags) and lists the checks, but it does not describe the input structure, nor does it provide guidance on when to use this tool versus siblings like sec_extract or deal_score. This leaves significant gaps for an agent to correctly invoke the tool.

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?

The schema has a single 'financials' object parameter with no description and 0% schema description coverage. The tool description does not explain the structure or required fields within the financials object, leaving the agent without guidance on how to construct valid input. This is a critical gap.

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 performs an 'institutional validation pass' over company financials, specifying concrete validation checks like tax-rate sanity, EBITDA/EBIT bridge, and growth continuity. This specific verb+resource structure distinguishes it from sibling tools like company_report (report generation), deal_score (scoring deals), and sec_extract (extracting SEC data).

Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.

Usage Guidelines3/5

Does the description explain when to use this tool, when not to, or what alternatives exist?

The description implies the tool is used for validating financial integrity, but it does not explicitly state when to use it over the sibling tools or provide exclusions. It lacks direct comparative guidance, so the usage context is implied rather than explicit.

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

A3.8/5.0
Disambiguation4/5

Each tool has a clear role: sec_extract retrieves raw data, validate_financials checks specific metrics, deal_score assesses a deal profile, and company_report produces a comprehensive report. Minor overlap exists between deal_score and validate_financials (both assess financial integrity), but descriptions distinguish them well enough.

Naming Consistency3/5

All names are snake_case and readable, but they mix noun-first (company_report, deal_score), verb-first (validate_financials), and abbreviated noun-verb (sec_extract) patterns. This inconsistency is noticeable but not confusing.

Tool Count5/5

Four tools is well-scoped for a financial analysis server: extraction, validation, deal scoring, and comprehensive reporting. Each tool earns its place without redundancy or bloat.

Completeness5/5

The tool set covers the full analysis pipeline: pull verified data (sec_extract), validate it (validate_financials), score specific deals (deal_score), and generate a comprehensive company report (company_report). No obvious dead ends or missing critical operations.

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