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Verify French Company

factrail_verify
Read-onlyIdempotent

Use when you need verified, source-backed facts about a French company from its SIREN or SIRET. Returns Evidence with provenance, freshness, coverage, conflicts, and a reusable receipt.

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
fieldsNoOptional company fields to resolve; omit for the default set.
identifierYesA 9-digit SIREN or 14-digit SIRET.
subject_typeYesCurrently only French companies are supported.

Output Schema

TableJSON Schema
NameRequiredDescriptionDefault
factsYes
statusYes
subjectYes
coverageYes
evidenceYes
conflictsYes
freshnessYes
receipt_idNo
generated_atYes
schema_versionNo1.2
state_fingerprintNo

Schema Changelog

Changes observed during successful MCP inspections.

  1. Changed3 schema fields changed
    • addedOutput schema / $defs / Fact / properties / support_level
      Added value: +{
      +  "$ref": "#/$defs/SupportLevel",
      +  "default": "supported"
      +}
    • addedOutput schema / $defs / SupportLevel
      Added value: +{
      +  "description": "How strongly a fact is supported, independent of numeric confidence.",
      +  "enum": [
      +    "authoritative",
      +    "supported",
      +    "derived_supported",
      +    "derived_provisional",
      +    "curated",
      +    "caller_input"
      +  ],
      +  "title": "SupportLevel",
      +  "type": "string"
      +}
    • changedOutput schema / properties / schema_version / default
      Previous value: -"1.1"New value: +"1.2"
  2. Added

TDQS

A3.5/5.0
Behavior3/5

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

Annotations already declare readOnlyHint, idempotentHint, openWorldHint and destructiveHint=false, so the safety profile is fully covered. The description adds that results carry provenance, freshness, coverage and conflicts, which is genuine behavioral context, but with an output schema present most of the return-value detail is already available elsewhere.

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?

Two tight sentences, with the usage trigger front-loaded before the return description. No filler, though "reusable receipt" is slightly vague and could carry its weight better with a concrete note on reuse.

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?

With rich annotations, a full-coverage schema and an output schema, the description only needed to convey purpose and the evidence-oriented nature of the response, which it does. The one real hole is routing between it and its overlapping siblings, which the description never resolves.

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

Parameters3/5

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

Schema description coverage is 100% and all three parameters are documented (identifier pattern, subject_type enum, fields list), so the schema carries the semantics. The description only echoes the SIREN/SIRET idea and adds no detail on the fields array or default field set, making the baseline 3 appropriate.

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?

Names a specific verb (verify) and resource (French company) plus the accepted keys (SIREN/SIRET), so the agent knows exactly what it does. However, it does not differentiate itself from the near-identical sibling verify_french_company, nor from analyze_company, so it falls short of the 5 bar for sibling differentiation.

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?

"Use when you need verified, source-backed facts" gives a usable trigger condition, which is more than nothing. But it names no exclusions and no alternatives, which matters a lot here because verify_french_company appears to be a directly overlapping sibling — the definition leaves the agent to guess which to call.

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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