talent_poaching_risk
Analyzes employee poaching risk for CHROs by evaluating LinkedIn profile activity (job searches, profile views) and comparing compensation against BLS benchmarks. Returns a ranked list of high-risk employees with risk scores and suggested retention actions. Ideal for proactive talent retention st...
This record as markdown: /tools/io-github-getgapup-mcp-knowledge/talent-poaching-risk.md
What talent_poaching_risk does on Mcp Knowledge
AI agents call talent_poaching_risk 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 |
|---|---|---|---|
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 |
location | string | — | Geographic location filter (e.g., 'San Francisco, CA') |
department | string | Yes | Department filter (e.g., 'Engineering', 'Sales') |
min_tenure_months | number | — | Minimum tenure in months to include in analysis |
benchmark_job_title | string | — | Specific job title for compensation benchmarking |
Parameters from the server's own tool schema.
Why talent_poaching_risk is rated Low
The tool retrieves and analyzes data (LinkedIn activity, compensation benchmarks, employee profiles) to generate risk assessments. This is fundamentally a Read operation—data retrieval and analysis with no side effects.
From the tool's definition Tool 'analyzes' and 'evaluates' LinkedIn profile activity and compensation data, 'returns a ranked list' of employees with risk scores. No modification, deletion, or execution of external systems described.
Attacks that exploit this kind of access
The rule that runs talent_poaching_risk 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_poaching_risk, this is the rule to start with:
talent_poaching_risk is read-only, so it stays allowed. Everything else on the server is denied unless you say otherwise.
The button opens the PolicyLayer dashboard: create your workspace, connect Mcp Knowledge, apply this rule, and every talent_poaching_risk call is checked against it from then on.
Questions about talent_poaching_risk
Analyzes employee poaching risk for CHROs by evaluating LinkedIn profile activity (job searches, profile views) and comparing compensation against BLS benchmarks. Returns a ranked list of high-risk employees with risk scores and suggested retention actions. Ideal for proactive talent retention strategies. Keywords: employee retention, poaching risk, compensation benchmark, LinkedIn activity, CHRO analytics. It is categorised as a Read tool in the Mcp Knowledge MCP Server, which means it retrieves data without modifying state.
talent_poaching_risk accepts 5 parameters: async, location, department, min_tenure_months, benchmark_job_title. Required: department. The full parameter table on this page comes from the server's own tool schema.
Register the Mcp Knowledge MCP server in PolicyLayer and add a rule for talent_poaching_risk: 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.
talent_poaching_risk is a Read tool with low risk. Read-only tools are generally safe to allow by default.
Yes. Add a rate_limit block to the talent_poaching_risk 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.
Set action: deny in the PolicyLayer policy for talent_poaching_risk. 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.
talent_poaching_risk 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.
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