HelpMyAgent

French Company Signals

company_fr_signals

Detects explainable business signals from available French company data, including closure, insolvency, revenue changes, new filings, public contracts and certifications. Use when: You need explainable business signals derived from existing public Company data rather than raw records from one source. You want to detect supported signals such as closure, insolvency, revenue change, new financial filings, public contracts or certifications. Avoid when: You need the underlying raw financial, BODACC, contract or certification records; call the specialized endpoint instead. You need continuous monitoring or push alerts; this endpoint evaluates signals at request time and is not a subscription service. Limitations: Only the signal families explicitly implemented by the endpoint are evaluated; it is not a general predictive business-risk model. Coverage can be partial when one of the underlying public-data services is unavailable. Price: 0.020 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
`exists`	Yes
`signals`	Yes
`coverage`	Yes
`checked_at`	Yes
`identifier`	Yes
`lookback_days`	Yes
`signals_count`	Yes
`identifier_type`	Yes

Schema Changelog

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

First observedSep 6, 2026

TDQS

A4.3/5.0

Behavior4/5

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

It adds useful behavioral context beyond annotations: evaluation happens at request time, it is not a subscription service, coverage can be partial when an underlying data source is unavailable, and it is not a general predictive risk model. The description's 'detects' language is consistent with annotations; no contradiction is apparent.

Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.

Conciseness3/5

Is the description appropriately sized, front-loaded, and free of redundancy?

The description is well structured with Use when, Avoid when, Limitations, and Price, and it front-loads the core purpose. However, the list of signal families is repeated nearly verbatim in the first sentence and the 'You want to detect supported signals...' sentence, which adds redundancy.

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 single-parameter endpoint with a provided output schema, this description covers selection criteria, exclusions, limitations, pricing, and request-time behavior. Nothing essential is missing for an agent to decide whether and how to call this tool.

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?

There is only one parameter, and the schema already fully describes it with a pattern and description, giving 100% schema coverage. The description adds only broad context about French company data, so the high-coverage 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?

The description opens with a specific verb and resource: 'Detects explainable business signals from available French company data,' naming concrete signal families. It also contrasts itself with raw-record siblings by saying these are signals derived from existing public data rather than raw records from one source. This makes the tool's scope unambiguous.

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?

The description has explicit 'Use when' and 'Avoid when' sections, directing agents to the specialized endpoint for raw records and ruling out use cases like continuous monitoring. This is actionable routing guidance that clearly distinguishes the tool from its siblings.

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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