Skip to main content
Glama

mcp-knowledge

by io.github.getgapup

candidate_screening_ranking

Read-onlyIdempotent

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.

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
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.
candidatesYesArray of candidate objects. Maximum 50.
role_countryNoOptional ISO 2-letter country code for regional context (informational).
job_descriptionYesFull text or summary of the job description and role requirements.
criteria_weightsNoOptional weighting per criterion. Default: skills=0.4, experience=0.2, education=0.1, progression=0.15, culture=0.15.

Output Schema

TableJSON Schema
NameRequiredDescriptionDefault
statusYes
sourcesYes
nice_to_haveYes
quality_scoreYes
required_skillsYes
candidates_rankedYes
diversity_signalsNo
shortlist_recommendedYes
job_description_summaryYes

TDQS

A4.3/5.0
Behavior4/5

Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?

Annotations declare readOnlyHint=true and destructiveHint=false, indicating safe read operation. Description adds valuable behavioral context: local processing, no external API calls, instant response, privacy-preserving. Also details output structure and ethical warning about diversity signals. No contradiction 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.

Conciseness5/5

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

Description is comprehensive yet concise, front-loaded with the main purpose and target audience. Every sentence adds value: listing criteria, output fields, diversity warning, and processing characteristics. No redundant or wasted words.

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 complexity of the tool (5 parameters, nested objects, output schema exists), the description is fully complete. It explains input requirements (job description, candidates), output structure (overall score, matched/missing skills, red/green flags, interview questions, fit_summary), and important ethical caveats (diversity signal proxy warnings). The existence of output schema helps but description adds necessary context.

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% with descriptions for all parameters, providing baseline of 3. Description adds significant meaning beyond schema by explaining the five weighted criteria (skills_match, experience_match, etc.) and their definitions, which are not detailed in the schema. Also describes per-candidate output fields like red_flags, green_flags, and fit_summary.

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?

Clearly states it is an AI-powered candidate screening and ranking tool for recruiters, hiring managers, ATS providers, and recruitment AI agents. Specifies exactly what it does: ingests job description and resumes, returns ranked shortlist with score breakdowns across five criteria. Differentiates from siblings by targeting recruitment use case, though not explicitly naming alternatives.

Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.

Usage Guidelines3/5

Does the description explain when to use this tool, when not to, or what alternatives exist?

Implied usage through description of functionality and emphasis on local processing, instant response, and privacy-preservation, suggesting it's suitable for sensitive data scenarios. However, no explicit guidance on when to use versus alternatives, nor when not to use this tool.

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

All mcp-knowledge toolsInspect
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

C2.8/5.0
Disambiguation2/5

Many tools have overlapping purposes, especially in competitive intelligence, ESG, and risk assessment. For example, there are multiple tools for competitor analysis (competitive_deep_dive, competitor_intel, competitor_moves, etc.) with unclear boundaries. Agents would struggle to select the correct tool without deep understanding of subtle differences.

Naming Consistency2/5

Tool names are a mix of English and French, and follow no consistent pattern. Some use snake_case (e.g., abm_architect, action_plan_esg), while others are verb-focused (e.g., content_catalog, fx_rate). The lack of a uniform naming convention makes it hard for agents to predict tool names.

Tool Count1/5

With 271 tools, the server is excessively large. Even for a broad knowledge domain, this number of tools makes discovery and selection inefficient. Typical coherent servers have 3-15 tools; this has an order of magnitude more, indicating poor scoping.

Completeness3/5

The tool set covers many domains (compliance, finance, marketing, HR, etc.), but the coverage is uneven due to redundancy. Key areas have multiple overlapping tools, while some sub-domains may still have gaps. Overall, the surface is broad but not well-curated.

Resources