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gapup-mcp

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?

The description discloses important behavioral details beyond the annotations: cash-only salary data, cache TTL of 24h, optional ONET_API_KEY, and static 2024 data with fallback. These add transparency about data freshness and configuration, though it does not cover every edge case.

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 with a clear intro and bullet points for each mode. Every sentence adds value, covering purpose, data sources, and constraints without unnecessary verbosity.

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 (four modes, multiple data sources), the description is remarkably complete. It covers data provenance, geographic scope, salary components, caching, and optional authentication, leaving few gaps for an agent to misinterpret.

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?

With 100% schema description coverage, the baseline is 3, but the description adds significant meaning by explaining each mode's output specifics (e.g., p25/median/p75, ESCO URI, similarity scores). This goes beyond the schema's brief mode definitions.

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 what the tool does: provides HR tech intelligence across four explicit modes (salary benchmark, skills taxonomy, job market trends, adjacent roles). Each mode is described with specific data sources and outputs, making it distinct from siblings.

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 gives clear context on intended users (CHROs, recruiters, VC teams) and enumerates the modes, which implicitly guides when to use it. However, it does not explicitly mention alternatives or exclusions, so it stops short of full guidance.

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

C2.4/5.0
Disambiguation1/5

Over 50 tools share the identical template 'Gapup agent-payable C-suite expertise' with similar French descriptions and reference cases, making their boundaries indistinguishable. Clusters like competitor_intel, competitive_deep_dive, competitor_moves, competitor_profiles, competitor_pricing_radar, competitor_pricing_scrape, and competitor_recommendations heavily overlap in purpose.

Naming Consistency1/5

Names are chaotic: mix of French and English, snake_case and camelCase, verb_noun, noun, and adjective forms with no uniform pattern. Examples like 'bp_narratif', 'content_enrichment', 'ai_governance_full_report_async', and 'job_result' show no coherent naming convention.

Tool Count1/5

271 tools is far beyond any reasonable MCP server scope, creating an overwhelming selection burden for agents. This count vastly exceeds the 25+ threshold for 'too many' and makes navigation impractical.

Completeness2/5

While the server covers many business domains, it lacks lifecycle operations (e.g., no update/delete tools for the deliverables it generates) and the input specifications are vague ('documented case fields' without documentation), creating functional dead ends. The sheer breadth does not compensate for these gaps.

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