Skip to main content
Glama

HelpMyAgent

Payment context for a French company

company_fr_payment_context

Returns structured public counterparty context before an automated B2B payment. It never approves or recommends a payment. Use when: Returns structured public counterparty context before an automated B2B payment. It never approves or recommends a payment. 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: 0.020 USD per call via x402.

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
domainNo
identifierYes

Output Schema

TableJSON Schema
NameRequiredDescriptionDefault
nameYes
sirenYes
existsYes
coverageYes
checked_atYes
identifierYes
legal_riskYes
limitationsYes
company_statusYes
identifier_typeYes
radiation_eventsYes
compliance_statusYes
public_alerts_foundYes
latest_relevant_eventYes
collective_procedure_eventsYes

Schema Changelog

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

  1. Added

TDQS

A3.6/5.0
Behavior4/5

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

Annotations already cover the safety profile; the description adds useful behavioral context: the endpoint is advisory only, coverage depends on public sources, and there is a per-call cost. No contradiction with annotations is present.

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 organized into useful sections, but the 'Use when' section repeats the opening two sentences verbatim, which is redundant. Limitations and price are valuable, but the duplication 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?

Given the output schema and annotations, the description covers the primary purpose, limitations, and cost. It remains incomplete because neither parameter is explained and no alternative tool is suggested, leaving the agent to infer the role of 'domain' and the identifier.

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 says nothing about the 'identifier' or optional 'domain' parameters. The required identifier's SIREN/SIRET semantics and the meaning of 'domain' must be guessed from the schema regex and parameter name, so the description adds no parameter-level meaning.

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 action ('Returns structured public counterparty context') and a clear use domain (automated B2B payment). It also immediately distinguishes itself from decision-oriented tools by saying it never approves or recommends a payment, separating it from siblings like risk, default_score, or verify.

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

Usage Guidelines4/5

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

Provides explicit 'Use when' and 'Avoid when' guidance: use before an automated B2B payment, avoid as a legal, regulated credit, or guaranteed fraud-free decision. However, it does not name alternative sibling tools, so it stops short of a 5.

Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.

Try in Browser

Glama MCP Gateway

Add one secure layer between your agents and this server.

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