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lookup_financials_fr

Annual accounts filed with the French RNE (INPI). Query: ?siren=552032534 (9 digits, Luhn-validated). Returns the filing history -- closing date, filing date, type (complet/simplifie/consolide), public or confidential -- which is a KYB signal in itself. For the latest public filing it adds headline figures (revenue, operating and net result, total assets, equity) when the liasse scheme is mapped, else says not_covered rather than guessing. 24h cache. Price: $0.03 USDC per call (x402).

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
sirenYes

TDQS

A4.1/5.0
Behavior4/5

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

With no annotations, the description carries full burden. It discloses key behaviors: returns filing history with specific fields, includes headline figures only when mapped else says 'not_covered', mentions 24h cache, and pricing. It does not cover error responses or rate limits, but provides substantial behavioral detail.

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 a single paragraph that conveys all essential information without unnecessary words. It could be slightly improved by using bullet points for the returned data, but it remains efficient and front-loaded with the core purpose.

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 tool has one simple parameter and no output schema, the description thoroughly explains the return data (filing history, headline figures, fallback behavior) and adds cache and pricing details. Minor gaps exist (e.g., error handling), but overall it is complete enough for an AI agent to use correctly.

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?

The input schema has 0% coverage, but the description adds full context for the 'siren' parameter: explains it is a 9-digit Luhn-validated number, provides an example, and describes how it is used in a query. This compensates well for the schema's lack of description.

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?

Description clearly states it retrieves annual accounts filed with the French RNE (INPI), specifying the resource (French RNE filings) and the action (lookup). The detail differentiates it from sibling tools like lookup_company_fr or lookup_company_uk, which focus on general company info or other countries.

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 when to use the tool (for French company financials) and shows the query format, but it does not explicitly state when not to use it or provide alternatives. Context from sibling names suggests differentiation, but the description alone lacks explicit usage boundaries.

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

A4.2/5.0
Disambiguation5/5

Each tool serves a distinct verification or lookup purpose (e.g., company lookup, sanctions screening, address geocoding, IBAN validation) with no overlap. Even tools targeting the same source (e.g., check_eori vs. validate_vat_eu) have clearly different inputs and outputs.

Naming Consistency4/5

Almost all tools follow a verb_noun pattern (verify, validate, lookup, check, find, screen) with clear nouns. The only exception is 'catalog' (a noun-only name), but it's a minor deviation that doesn't cause confusion.

Tool Count5/5

19 tools is appropriate for a data verification service covering company data, sanctions, VAT, addresses, emails, IBANs, and financials. Each tool addresses a specific need without being excessive or sparse.

Completeness5/5

The tool set covers the full lifecycle of EU company verification: existence, status, financials, ownership, VAT, sanctions, EORI, invoices, tenders, and address validation. No obvious gaps for its stated domain.