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Public-contract competitors

company_fr_public_contract_competitors

Finds companies winning public contracts on CPV segments shared with the target company using DECP public award data. Use when: Finds companies winning public contracts on CPV segments shared with the target company using DECP public award data. Avoid when: Do not interpret inferred matches as official competitive relationships or identity proof. Limitations: Coverage depends on the listed public sources. Price: 0.020 USD per call via x402. Accepted x402 networks: eip155:8453, xrpl:0.

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
limitNo
identifierYes

Output Schema

TableJSON Schema
NameRequiredDescriptionDefault
nameYes
sirenYes
existsYes
identifierYes
competitorsYes
limitationsNo
cpv_segmentsYes

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?

Annotations provide little safety context, so the description carries the burden. It adds valuable behavioral context: matches are inferred and should not be treated as official competitive relationships, and coverage depends on listed public sources. It also reveals operational constraints such as pricing and accepted networks.

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

Conciseness2/5

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

The opening sentence is clear and front-loaded, but the 'Use when' line duplicates it word-for-word, adding no value. The inclusion of price and network details is useful, but the repetition makes the description feel padded rather than concise.

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 presence of an output schema reduces the need to explain return values, but the description still leaves input semantics vague and provides no routing guidance among the many related siblings. An agent would struggle to know exactly what identifier format is expected or when to choose this over company_fr_public_contracts or company_fr_competitors.

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%, but the description does not name or explain either parameter. 'Target company' implies the identifier's role, but the format and pattern are left to schema inference, and the limit parameter's meaning is not described at all.

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 states a specific verb ('Finds') with a precise object: companies winning public contracts on CPV segments shared with the target company, using DECP public award data. This clearly distinguishes it from siblings like company_fr_public_contracts and company_fr_competitors.

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?

The 'Use when' section merely repeats the tool's function verbatim instead of providing decision context. No alternative tools are named, and 'Avoid when' is an interpretive caution about output reliability rather than a usage exclusion.

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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