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Benchmark a French company against public peers

company_fr_benchmark

Benchmarks a company against public peers using available revenue, net income, growth and margin metrics. Use when: Benchmarks a company against public peers using available revenue, net income, growth and margin metrics. 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: 0.040 USD per call via x402.

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
scopeNoregion
identifierYes
peer_limitNo

Output Schema

TableJSON Schema
NameRequiredDescriptionDefault
nameYes
peersYes
scopeYes
sirenYes
existsYes
metricsYes
identifierYes
peer_countYes
limitationsYes

Schema Changelog

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

  1. Added

TDQS

C2.8/5.0
Behavior3/5

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

Annotations already provide readOnlyHint, openWorldHint, idempotentHint, and destructiveHint, so the description does not need to restate those. The description adds useful limitations about coverage depending on public sources, a price note, and a legal-use disclaimer. It does not explain edge behaviors like missing data handling or latency, but the output schema and annotations carry some of that burden.

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 reasonably short and uses labeled sections, which aids scanning. However, the 'Use when' line is a verbatim duplicate of the first sentence, making it redundant. The limitations and price sections are useful and earn their place, but the redundancy keeps this from being a tightly written definition.

Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.

Completeness2/5

Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?

The tool has three parameters, one required, and an output schema, so the description does not need to detail return values. However, it leaves parameter semantics unexplained and provides no sibling differentiation within a large family of company_fr_* tools. An agent selecting between benchmark, peers, compare, and competitors would need more context to invoke this tool correctly.

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

Parameters2/5

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

Schema description coverage is 0%, so the description carries full responsibility for explaining parameters. It does not define identifier, scope, or peer_limit. While peer_limit can be inferred from 'public peers' and scope enum values are self-explanatory, identifier type and meaning are unclear beyond the regex pattern. The description fails to compensate for the lack of schema 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 states the tool benchmarks a company against public peers and lists the specific metrics used (revenue, net income, growth, margin). This is a specific verb+resource combination that conveys the core function. However, it does not distinguish this tool from siblings like company_fr_peers or company_fr_compare, which could plausibly overlap.

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

Usage Guidelines2/5

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

There is a 'Use when' section, but it merely repeats the main benchmark sentence and adds no new routing information. The 'Avoid when' section only addresses legal, regulated credit, and fraud-free decisions rather than identifying when to prefer an alternative tool. No guidance is given for choosing between company_fr_benchmark, company_fr_peers, or company_fr_compare.

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