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mcp-knowledge

by io.github.getgapup

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
NameRequiredDescriptionDefault
modeYesMode d'analyse : 'company_hiring' | 'role_market' | 'competitor_hiring_comparison'
roleNoIntitulé de poste à analyser (pour role_market, ex. 'data scientist', 'compliance officer')
asyncNoIf 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.
companyNoNom de la société (pour company_hiring ou comme 1er concurrent)
locationNoPays ou ville (ex. 'France', 'United States', 'London')
competitorsNoListe de sociétés à comparer (pour competitor_hiring_comparison, min 2)
period_daysNoFenêtre d'analyse en jours (défaut 30)

Output Schema

TableJSON Schema
NameRequiredDescriptionDefault
modeYes
statusYes
sourcesYes
role_marketNo
quality_scoreYes
company_hiringNo
competitor_comparisonNo

TDQS

A4.7/5.0
Behavior5/5

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

Beyond annotations (readOnlyHint, etc.), the description adds critical behavioral details: it's read-only aggregation, has 6-hour caching, SLA ≤15s, async support, and lists data sources. No contradictions with annotations.

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

Conciseness5/5

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

The description is a single, well-structured paragraph that efficiently conveys all key information without redundancy. Every sentence adds value, and the numbered modes improve readability.

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

Completeness5/5

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

Given the tool's complexity (3 modes, multiple parameters, output schema), the description covers purpose, modes, sources, usage, caching, SLA, and async option comprehensively. It provides all necessary context for correct 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 has 100% coverage with parameter descriptions. The description adds value by explaining the three modes and how parameters like role, company, competitors relate to each mode, enhancing understanding beyond schema alone.

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 aggregates public job postings to infer recruitment trends, lists three specific modes (company_hiring, role_market, competitor_hiring_comparison) with details, and differentiates from siblings by its unique focus on hiring intelligence.

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 provides explicit use cases (due diligence VC, competitive intelligence, HR benchmarks, strategic pivot signals) and mentions sources and caching. It lacks explicit when-not-to-use guidance or alternative tool names, but the context is clear enough for appropriate selection.

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

C2.8/5.0
Disambiguation2/5

Many tools have overlapping purposes, especially in competitive intelligence, ESG, and risk assessment. For example, there are multiple tools for competitor analysis (competitive_deep_dive, competitor_intel, competitor_moves, etc.) with unclear boundaries. Agents would struggle to select the correct tool without deep understanding of subtle differences.

Naming Consistency2/5

Tool names are a mix of English and French, and follow no consistent pattern. Some use snake_case (e.g., abm_architect, action_plan_esg), while others are verb-focused (e.g., content_catalog, fx_rate). The lack of a uniform naming convention makes it hard for agents to predict tool names.

Tool Count1/5

With 271 tools, the server is excessively large. Even for a broad knowledge domain, this number of tools makes discovery and selection inefficient. Typical coherent servers have 3-15 tools; this has an order of magnitude more, indicating poor scoping.

Completeness3/5

The tool set covers many domains (compliance, finance, marketing, HR, etc.), but the coverage is uneven due to redundancy. Key areas have multiple overlapping tools, while some sub-domains may still have gaps. Overall, the surface is broad but not well-curated.

Resources