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HelpMyAgent

Explainable public default-risk indicator

company_fr_default_score

Returns a deterministic, explainable HelpMyAgent public-data default-risk indicator. It is not an official credit score. Use when: Returns a deterministic, explainable HelpMyAgent public-data default-risk indicator. It is not an official credit score. Avoid when: Do not use this endpoint as a legal, regulated credit or guaranteed fraud-free decision unless explicitly stated otherwise. Limitations: Coverage depends on the public sources listed for this endpoint. Price: 0.075 USD per call via x402.

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
identifierYes

Output Schema

TableJSON Schema
NameRequiredDescriptionDefault
nameYes
levelYes
scoreYes
sirenYes
existsYes
componentsYes
confidenceYes
identifierYes
limitationsYes
identifier_typeYes
known_weight_percentNo

Schema Changelog

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

  1. Added

TDQS

B3.2/5.0
Behavior4/5

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

The description adds behavioral context beyond the annotations: the indicator is deterministic and explainable, not an official credit score, and coverage depends on public sources. The deterministic claim is in mild tension with idempotentHint=false and openWorldHint=true, but the coverage limitation explains that the underlying public data can change. No direct annotation contradiction exists.

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 short and front-loaded with the core purpose, and the Avoid/Limitations/Price sections are efficient. However, the 'Use when' section is a verbatim repetition of the opening sentence, adding no information and wasting a line. This redundancy prevents a higher score.

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?

An output schema exists, so return-value documentation is not necessary, and the description covers important caveats: non-official status, legal-use restriction, source-dependent coverage, and price. But it omits parameter semantics and does not situate the tool relative to sibling risk-related tools, so an agent may still be unsure how to invoke it correctly or choose it confidently.

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

Parameters1/5

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

Schema description coverage is 0% and the description never explains what the identifier parameter means, what formats are accepted, or that it refers to a French company identifier such as SIREN/SIRET. The regex pattern in the schema is the only hint, but the description does nothing to compensate for the lack of parameter documentation.

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 identifies a specific deliverable: a deterministic, explainable public-data default-risk indicator, and explicitly disclaims that it is not an official credit score. This gives an agent a solid sense of what the tool does. However, it does not explicitly differentiate this tool from the sibling company_fr_risk, leaving some overlap ambiguity.

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 includes an explicit 'Avoid when' clause stating not to use it for legal, regulated credit, or guaranteed fraud-free decisions, and it mentions source-dependent coverage limitations. The 'Use when' section, however, simply repeats the main description rather than providing a concrete trigger or routing to an alternative tool, so the guidance is only partially useful.

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.

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