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verify_address_fr

French address geocoding on the official Base Adresse Nationale (api-adresse.data.gouv.fr, Etalab/IGN, continuously updated). Forward: ?q=8 boulevard du port amiens with optional &type=housenumber|street|locality|municipality &citycode=80021 (INSEE filter). Reverse: ?lat=49.897&lon=2.290. &limit=1-10 (default 3). Each match returns normalized label, confidence score, lat/lon, street, postcode, city, INSEE citycode and context. No match returns 400 (not billed). Cached 24h. Price: $0.001 USDC per call (x402).

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
qYes
limitNo

TDQS

B3.2/5.0
Behavior3/5

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

The description discloses forward and reverse geocoding, return fields (label, confidence, lat/lon, etc.), caching (24h), pricing, and no-match behavior (400, not billed). However, the schema lacks parameters like type and citycode mentioned in the description, creating inconsistency and potential confusion for the AI agent.

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 well-structured with forward/reverse sections and logical order (parameters, returns, caching, pricing). Every sentence adds information, though it could be slightly more concise by avoiding parameter details not in the schema.

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

Completeness3/5

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

Given no output schema, the description adequately explains return values and behavior. However, the schema mismatch and missing details on authentication or error handling (beyond 400) leave gaps for a moderately complex geocoding tool.

Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.

Parameters2/5

Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?

With 0% schema description coverage, the description adds value through examples for q and limit range (1-10, default 3). However, it also describes parameters (type, citycode, lat, lon) not present in the schema, contradicting the input structure and undermining reliability.

Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.

Purpose4/5

Does the description clearly state what the tool does and how it differs from similar tools?

The description clearly states it performs French address geocoding using the official Base Adresse Nationale, distinguishing it from sibling tools like lookup_company_fr. However, the mention of parameters not in the schema (type, citycode, lat, lon) introduces ambiguity, slightly reducing clarity.

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?

No explicit guidance on when to use this tool versus alternatives. The purpose is implied through the description (geocoding French addresses), but there is no comparison to siblings or conditions for use, making it adequate but minimally helpful.

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.