gapup-mcp

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

Name	Required	Description
`mode`	Yes	Analysis mode: salary_benchmark=compensation data, skills_taxonomy=ESCO/O*NET mapping, job_market_trends=market growth and demand, adjacent_roles=career path recommendations.
`role`	No	Job title (required for salary_benchmark, job_market_trends, adjacent_roles). Examples: "Senior Software Engineer", "Compliance Officer", "Data Scientist", "CFO".
`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.
`skill`	No	Skill to classify (required for skills_taxonomy mode). Examples: "Python", "transformer architecture", "GDPR", "Kubernetes", "leadership".
`country`	No	ISO 2-letter country code. Default: US. Examples: US, FR, DE, GB, SG.
`seniority`	No	Seniority level. Default: senior. Affects salary benchmark ranges.

Output Schema

TableJSON Schema

Name	Required	Description	Default
`mode`	Yes
`status`	Yes
`sources`	Yes
`quality_score`	Yes
`adjacent_roles`	No
`skills_taxonomy`	No
`salary_benchmark`	No
`job_market_trends`	No

TDQS

A4.3/5.0

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 behavioral details: data is static 2024 with BLS fallback, cache TTL 24h, optional ONET_API_KEY, and that salary data excludes equity/RSU/bonus. 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.

Conciseness4/5

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

The description is well-structured with bullet points for each mode, making it scannable. It front-loads the purpose and target audience. While somewhat long, every sentence adds value and there is no redundancy.

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 (four modes, multiple parameters, and an output schema), the description covers each mode, data sources, constraints (cash-only, cache TTL), and includes examples. It provides sufficient context for an agent to understand the tool's capabilities without gaps.

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%, but the description adds meaningful context for each parameter: role examples, skill examples, and ISO country code format. The mode enumeration is expanded with detailed descriptions of each mode's purpose and data sources, aiding the agent in selecting the correct mode.

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, each with specific use cases. It distinguishes itself from siblings by naming concrete data sources (ESCO, O*NET, BLS OES, Levels.fyi) and target audiences (CHROs, recruiters, VC teams, etc.).

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 explains when to use each mode with detailed examples (e.g., 'salary_benchmark — cash-only salary medians...'). It provides context for selecting the appropriate mode but does not explicitly say when NOT to use or compare to alternative tools among the siblings.

Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.

All gapup-mcp tools Inspect

Try in Browser

RayrunBuild, deploy, and operate MCP servers without becoming an infrastructure team.ray.runAd

Glama MCP Gateway

Add one secure layer between your agents and this server.

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.

Resources

Need Help?