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mcp-knowledge

by io.github.getgapup

talent_intelligence

Read-only

HR tech intelligence for CHROs, recruiters, VC teams, comp & benefits leads and workforce planners. Four modes powered by ESCO, O*NET, BLS OES and crowd-sourced salary data:

• salary_benchmark — cash-only salary medians (p25/median/p75) for 54+ roles across US/EU/Asia. Covers tech, finance, compliance, healthcare, marketing, ops and C-suite. Data from BLS OES, Levels.fyi and StackOverflow Developer Survey 2024. • skills_taxonomy — maps a skill to its ESCO URI, O*NET codes, skill type (hard/soft/knowledge/cert), 8 related skills with similarity scores and typical roles. • job_market_trends — YoY growth %, open positions estimate, top employers and leading skills per job category × country. Static 2024 data with BLS baseline fallback. • adjacent_roles — up to 6 roles adjacent to a source role with ESCO taxonomy adjacency: similarity score, salary delta % and skills overlap %.

All salary data is cash-only (excludes equity/RSU/bonus). Cache TTL: 24h (stable labour market data). Optional env ONET_API_KEY for authenticated O*NET lookups (free registration at onetcenter.org).

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
modeYesAnalysis mode: salary_benchmark=compensation data, skills_taxonomy=ESCO/O*NET mapping, job_market_trends=market growth and demand, adjacent_roles=career path recommendations.
roleNoJob title (required for salary_benchmark, job_market_trends, adjacent_roles). Examples: "Senior Software Engineer", "Compliance Officer", "Data Scientist", "CFO".
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.
skillNoSkill to classify (required for skills_taxonomy mode). Examples: "Python", "transformer architecture", "GDPR", "Kubernetes", "leadership".
countryNoISO 2-letter country code. Default: US. Examples: US, FR, DE, GB, SG.
seniorityNoSeniority level. Default: senior. Affects salary benchmark ranges.

Output Schema

TableJSON Schema
NameRequiredDescriptionDefault
modeYes
statusYes
sourcesYes
quality_scoreYes
adjacent_rolesNo
skills_taxonomyNo
salary_benchmarkNo
job_market_trendsNo

TDQS

A4.5/5.0
Behavior4/5

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

Annotations already indicate readOnlyHint=true and destructiveHint=false. The description adds valuable details: data is stable labour market data, cash-only, 24h cache TTL, and mention of an async parameter for slow queries. No rate limits or auth details beyond optional API key, but sufficient given 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 well-structured: a concise opening line followed by clear bullet points for each mode. Information is front-loaded with purpose and then details. Every sentence adds value (data sources, caveats, usage guidance), no fluff.

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 existence of an output schema (inferred), the description adequately covers all necessary context: mode-specific data sources, parameter constraints, cache behavior, and optional API key. The tool's complexity is fully addressed without missing critical information.

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?

Input schema has 100% coverage with descriptions for all 6 parameters. The description adds semantic value by explaining the four mode options and their specific parameter requirements (e.g., role, skill, country, seniority). It enhances understanding of how parameters relate to modes beyond the schema.

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 provides HR tech intelligence with four distinct modes (salary_benchmark, skills_taxonomy, job_market_trends, adjacent_roles). Each mode is explained with specific data sources (BLS OES, ESCO, O*NET, Levels.fyi) and use cases, differentiating it from sibling tools like comp_benchmark_geo_delta or global_salary_inflation_adjuster.

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 usage context: four modes with bullet points detailing when each is relevant, caveats like cash-only salary data, cache TTL, and optional ONET_API_KEY. However, it does not explicitly compare to sibling tools or state when not to use this tool over alternatives like comp_benchmark_geo_delta.

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.

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