candidate_screening_ranking

SERVERMcp Knowledge SOURCEhttps://mcp.gapup.io

Low RISK CLASS

Category Read

Parameters 52 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/candidate-screening-ranking.md

WHAT IT DOES

What candidate_screening_ranking does on Mcp Knowledge

AI agents call candidate_screening_ranking 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
`candidates`	array	Yes	Array of candidate objects. Maximum 50.
`role_country`	string	—	Optional ISO 2-letter country code for regional context (informational).
`job_description`	string	Yes	Full text or summary of the job description and role requirements.
`criteria_weights`	object	—	Optional weighting per criterion. Default: skills=0.4, experience=0.2, education=0.1, progression=0.15, culture=0.15.

Parameters from the server's own tool schema.

RISK

Why candidate_screening_ranking is rated Low

The tool reads and analyzes input data (job descriptions and resumes) and returns ranked results with scoring breakdowns. It does not create, modify, delete, execute code, or move money — it is fundamentally a read/query/analysis operation.

From the tool's definition AI-powered candidate screening and ranking... Ingests a job description and 1-50 candidate resumes, returning a ranked shortlist with score breakdowns... Per candidate: overall_score 0-100, matched/

Risk signalsHigh parameter count (15 properties)

Attacks that exploit this kind of access

RECOMMENDED RULE

The rule that runs candidate_screening_ranking 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 candidate_screening_ranking, this is the rule to start with:

candidate_screening_ranking Allowed

candidate_screening_ranking 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": {
    "candidate_screening_ranking": {}
  }
}

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 candidate_screening_ranking call is checked against it from then on.

FAQ

Questions about candidate_screening_ranking

What does the candidate_screening_ranking tool do? +

AI-powered candidate screening and ranking for recruiters, hiring managers, ATS providers and recruitment AI agents. Ingests a job description and 1-50 candidate resumes, returning a ranked shortlist with score breakdowns across five weighted criteria: skills_match (tech stack and soft skills extracted from JD vs resume), experience_match (years vs seniority level inferred from JD), education_match (degree level + top-school detection), role_progression (Junior to Senior to Lead patterns), culture_fit_estimate (remote/hybrid, startup vs enterprise). Per candidate: overall_score 0-100, matched/missing skills, red_flags (job hopping, employment gaps, seniority mismatch), green_flags (long tenure, promotions), 3-5 interview questions, fit_summary. Diversity signals are first-name proxies ONLY with mandatory ethical WARNING. All processing is local -- no external API calls, instant response, privacy-preserving. It is categorised as a Read tool in the Mcp Knowledge MCP Server, which means it retrieves data without modifying state.

What parameters does candidate_screening_ranking accept? +

candidate_screening_ranking accepts 5 parameters: async, candidates, role_country, job_description, criteria_weights. Required: candidates, job_description. The full parameter table on this page comes from the server's own tool schema.

How do I enforce a policy on candidate_screening_ranking? +

Register the Mcp Knowledge MCP server in PolicyLayer and add a rule for candidate_screening_ranking: 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 candidate_screening_ranking? +

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

Can I rate-limit candidate_screening_ranking? +

Yes. Add a rate_limit block to the candidate_screening_ranking 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 candidate_screening_ranking completely? +

Set action: deny in the PolicyLayer policy for candidate_screening_ranking. 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 candidate_screening_ranking? +

candidate_screening_ranking 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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