gapup-mcp

job_postings_intelligence

Read-only

Agrégation d'offres d'emploi publiques pour inférer les tendances de recrutement. Trois modes : (1) company_hiring — analyse des postings d'une société : volume, fonctions (engineering/sales/marketing/ops/finance/hr), seniorité, géographie, croissance vs période précédente, signaux stratégiques inférés ; (2) role_market — volume marché global pour un rôle (open positions estimate, top employeurs, compétences demandées, médiane seniorité) ; (3) competitor_hiring_comparison — comparaison multi-sociétés (total postings, growth%, focus areas). Sources : Adzuna (ADZUNA_APP_ID/KEY env), RemoteOK (keyless), Himalayas (keyless), baseline statique 40 top employeurs. Usages : due diligence VC, intelligence compétitive, benchmarks RH, signaux pivots stratégiques. Cache 6h. SLA ≤15s.

Input Schema

TableJSON Schema

Name	Required	Description
`mode`	Yes	Mode d'analyse : 'company_hiring' \| 'role_market' \| 'competitor_hiring_comparison'
`role`	No	Intitulé de poste à analyser (pour role_market, ex. 'data scientist', 'compliance officer')
`async`	No	If true, returns a job_id immediately (<200ms) instead of waiting for the result. Poll the result with job_result(job_id). Use for slow tools to avoid client timeouts.
`company`	No	Nom de la société (pour company_hiring ou comme 1er concurrent)
`location`	No	Pays ou ville (ex. 'France', 'United States', 'London')
`competitors`	No	Liste de sociétés à comparer (pour competitor_hiring_comparison, min 2)
`period_days`	No	Fenêtre d'analyse en jours (défaut 30)

Output Schema

TableJSON Schema

Name	Required	Description	Default
`mode`	Yes
`status`	Yes
`sources`	Yes
`role_market`	No
`quality_score`	Yes
`company_hiring`	No
`competitor_comparison`	No

TDQS

A4.3/5.0

Behavior4/5

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

Annotations (readOnlyHint, openWorldHint) are complemented by behavioral details: cache 6h, SLA ≤15s, and async parameter (though not described in description, it's in schema). No contradiction. The description adds value beyond structured fields by explaining data freshness and performance expectations.

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

Conciseness4/5

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

The description is detailed and well-structured, using bullet points for modes. While concise for the amount of information, it could be slightly trimmed (e.g., removing 'Usages' redundancy). However, it remains efficient and easy to parse for an AI agent.

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

Completeness4/5

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

Given the tool's complexity (7 parameters, 3 modes, external data sources), the description covers purpose, modes, sources, cache, SLA, and intended use cases. An output schema exists, so return values are not required. It is comprehensive enough for correct tool selection and invocation.

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

Parameters4/5

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

Schema coverage is 100% with descriptions, providing a baseline of 3. The description enriches parameter understanding by explaining each mode's purpose and linking parameters to modes (e.g., 'company' for company_hiring, 'competitors' for competitor_hiring_comparison). It adds context beyond the schema's basic field descriptions.

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 clearly states the tool's purpose: aggregating public job postings to infer recruitment trends. It specifies three distinct modes (company_hiring, role_market, competitor_hiring_comparison) and each mode's function, effectively distinguishing it from a large set of sibling tools.

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?

The description lists explicit use cases (due diligence VC, competitive intelligence, HR benchmarks, strategic pivot signals) and data sources (Adzuna, RemoteOK, Himalayas, static baseline). It does not explicitly state when not to use or name alternative sibling tools, but the provided modes and context give sufficient guidance for appropriate invocation.

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

C2.5/5.0

Disambiguation2/5

With 271 tools, many have overlapping purposes (e.g., multiple competitor intel tools, multiple financial modelers, multiple ESG auditors). Detailed descriptions help slightly, but the sheer volume creates confusion. Agents would struggle to select the right tool among many similar options.

Naming Consistency1/5

Tool names are wildly inconsistent: mix of English and French, snake_case and short phrases, some very generic (process, run, execute equivalents). No discernible naming convention (e.g., abm_architect vs. boundary_control vs. bp_narratif). This makes it hard to predict tool names.

Tool Count1/5

271 tools is far beyond typical well-scoped servers (3-15). This indicates an unfocused, over-bloated tool surface. Even for a general business intelligence server, this number is excessive and violates the principle of each tool earning its place.

Completeness2/5

Despite the large count, coverage feels scattered. Some domains (e.g., content, competitive intel) have many tools, while others (e.g., supply chain, HR) have gaps. The set lacks a coherent scope; it seems like a dump of many separate tool collections rather than a complete, curated surface.

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