HelpMyAgent

French Company Legal Risk

company_fr_risk

Detects public legal-risk signals for a French company from company status and BODACC notices, including insolvency proceedings and deregistrations. This is not a credit score. Use when: You need explainable public legal-risk signals for a French company, including insolvency proceedings, deregistrations and administrative closure. You need a normalized low, warning, high or unknown legal-risk level backed by public events. Avoid when: You need a credit score, probability of default, payment behavior or bank-grade solvency assessment. You need sanctions or regulatory blacklist screening; use company/fr/compliance. Limitations: The BODACC analysis uses a 10-year lookback and reports when source coverage is insufficient to conclude. This is a public legal-risk signal service, not a credit score or legal opinion. Price: 0.010 USD per call via x402.

Input Schema

TableJSON Schema

Name	Required	Description	Default
`identifier`	Yes	9-digit SIREN or 14-digit SIRET

Output Schema

TableJSON Schema

Name	Required	Description	Default
`name`	Yes
`siren`	Yes
`events`	Yes
`exists`	Yes
`signals`	Yes
`identifier`	Yes
`risk_level`	Yes
`latest_event`	Yes
`company_status`	Yes
`lookback_years`	Yes
`identifier_type`	Yes
`assessment_basis`	Yes
`coverage_complete`	Yes

Schema Changelog

Changes observed during successful MCP inspections. Dates show when Glama detected each change.

First observedSep 6, 2026

TDQS

A4.4/5.0

Behavior4/5

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

Annotations provide little behavioral signal, so the description carries the burden. It adds meaningful context: 10-year BODACC lookback, reporting when source coverage is insufficient, normalized low/warning/high/unknown levels, and explicit caveats that this is not a credit score or legal opinion. No side-effect statement, but the tool is clearly a detection/read-style service.

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 clear Use/Avoid/Limitations sections and every part carries useful information. The phrase 'not a credit score' is repeated three times, which is slightly redundant but reinforces an important boundary.

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?

For a one-parameter tool with an output schema available, the description covers purpose, selection criteria, alternatives, limitations, pricing, and a high-level description of the result. Nothing needed for correct invocation is missing.

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 coverage is 100% and the single parameter 'identifier' is already described with a pattern and SIREN/SIRET explanation. The description adds no further parameter-level semantics beyond implying the company is French, so the baseline of 3 is appropriate.

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?

States a specific verb ('detects') and resource ('public legal-risk signals for a French company'), with concrete examples such as insolvency proceedings and deregistrations. Also explicitly excludes credit scoring, which helps distinguish this tool from financial-risk siblings.

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

Usage Guidelines5/5

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

Contains explicit 'Use when' and 'Avoid when' sections with concrete conditions. It names the alternative for sanctions/blacklist screening (company/fr/compliance) and lists excluded use cases, so an agent can route correctly without inference.

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

B3.3/5.0

Disambiguation3/5

Most endpoints target distinct resources, but several clusters are easy to confuse: company_fr_intelligence vs company_fr_kyb, company_fr_peers vs company_fr_competitors vs company_fr_public_contract_competitors, and company_fr_risk vs company_fr_default_score vs company_fr_payment_context. The descriptive names help, but the repetitive 'Use when' sections often restate the description rather than contrasting with nearby tools.

Naming Consistency4/5

The dominant convention is domain_fr_feature with consistent snake_case, e.g., company_fr_profile, company_fr_financials, company_fr_public_contracts, procurement_fr_search, which makes the family predictable. The three meta tools (describe_api, list_categories, search_apis) switch to a bare verb_noun style, and a few company_fr names use verbs while most use nouns, creating a minor inconsistency.

Tool Count2/5

With 30 tools, the surface exceeds the 25+ threshold and feels heavy for an agent to navigate, especially because aggregators like company_fr_intelligence and company_fr_kyb overlap with many single-purpose endpoints. The broad French-company data domain justifies a large number of endpoints, but several could be consolidated or split out to make the server more focused.

Completeness4/5

The set covers discovery, verification, profile, directors, financials, legal risk, compliance, public contracts, procurement, funding, benchmarking, signals, and aggregation, so core French-company workflows have no major dead ends. Minor gaps remain around beneficial-ownership/shareholder data and subscription-style monitoring, but those are explicitly outside the stated scope of most endpoints.

Resources

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