talent_intelligence

SERVERMcp Knowledge SOURCEhttps://mcp.gapup.io

Low RISK CLASS

Category Read

Parameters 61 required

Recommended Allowedsee the rule below

Registry record Grade F, identity unverified Pull the record →

This record as markdown: /tools/io-github-getgapup-mcp-knowledge/talent-intelligence.md

WHAT IT DOES

What talent_intelligence does on Mcp Knowledge

AI agents call talent_intelligence to retrieve information from Mcp Knowledge without modifying anything. It is typically the context-gathering step in research, monitoring, and reporting workflows, before the agent takes action elsewhere.

Parameter	Type	Required	Description
`mode`	string	Yes	Analysis mode: salary_benchmark=compensation data, skills_taxonomy=ESCO/O*NET mapping, job_market_trends=market growth and demand, adjacent_roles=career path re
`role`	string	—	Job title (required for salary_benchmark, job_market_trends, adjacent_roles). Examples: "Senior Software Engineer", "Compliance Officer", "Data Scientist", "CFO
`async`	boolean	—	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 ti
`skill`	string	—	Skill to classify (required for skills_taxonomy mode). Examples: "Python", "transformer architecture", "GDPR", "Kubernetes", "leadership".
`country`	string	—	ISO 2-letter country code. Default: US. Examples: US, FR, DE, GB, SG.
`seniority`	string	—	Seniority level. Default: senior. Affects salary benchmark ranges.

Parameters from the server's own tool schema.

RISK

Why talent_intelligence is rated Low

All described modes are read-only data retrieval operations: benchmarking salary data, mapping skills taxonomies, and querying job market trends. No writes, executions, deletions, or financial transactions are performed. The tool fetches and returns analytical intelligence from external datasets.

From the tool's definition HR tech intelligence... salary_benchmark, skills_taxonomy, job_market_trends — data retrieval modes powered by ESCO, O*NET, BLS OES and crowd-sourced salary data

Attacks that exploit this kind of access

RECOMMENDED RULE

The rule that runs talent_intelligence safely

PolicyLayer is an MCP gateway: it sits between your AI agents and Mcp Knowledge, and checks every tool call against a rule you set before the call runs. Nothing changes on the server itself. For talent_intelligence, this is the rule to start with:

talent_intelligence Allowed

talent_intelligence is read-only, so it stays allowed. Everything else on the server is denied unless you say otherwise.

View as policy code

policy.json

{
  "version": "1",
  "default": "deny",
  "tools": {
    "talent_intelligence": {}
  }
}

ALLOW ONLY THIS TOOL → Instant setup, no code required.

The button opens the PolicyLayer dashboard: create your workspace, connect Mcp Knowledge, apply this rule, and every talent_intelligence call is checked against it from then on.

FAQ

Questions about talent_intelligence

What does the talent_intelligence tool do? +

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). It is categorised as a Read tool in the Mcp Knowledge MCP Server, which means it retrieves data without modifying state.

What parameters does talent_intelligence accept? +

talent_intelligence accepts 6 parameters: mode, role, async, skill, country, seniority. Required: mode. The full parameter table on this page comes from the server's own tool schema.

How do I enforce a policy on talent_intelligence? +

Register the Mcp Knowledge MCP server in PolicyLayer and add a rule for talent_intelligence: allow, deny, rate-limit, or require approval. Point your MCP client at the PolicyLayer proxy URL and the rule is enforced on every call, before it reaches Mcp Knowledge. Nothing to install.

What risk level is talent_intelligence? +

talent_intelligence is a Read tool with low risk. Read-only tools are generally safe to allow by default.

Can I rate-limit talent_intelligence? +

Yes. Add a rate_limit block to the talent_intelligence rule in your PolicyLayer policy. For example, setting max: 10 and window: 60 limits the tool to 10 calls per minute. Rate limits are tracked per agent session and reset automatically.

How do I block talent_intelligence completely? +

Set action: deny in the PolicyLayer policy for talent_intelligence. The AI agent will receive a policy violation error and cannot call the tool. You can also include a reason field to explain why the tool is blocked.

What MCP server provides talent_intelligence? +

talent_intelligence is provided by the Mcp Knowledge MCP server (https://mcp.gapup.io). PolicyLayer sits as a proxy in front of this server to enforce policies before tool calls reach the server.

KEEP EXPLORING

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