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gapup-mcp

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

A4.7/5.0
Behavior5/5

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

Annotations already declare readOnlyHint=true and destructiveHint=false, but the description adds valuable behavioral context: 'All processing is local -- no external API calls, instant response, privacy-preserving' and the diversity signals caveat with a 'mandatory ethical WARNING.' These go beyond what annotations provide, disclosing privacy and ethical behaviors.

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?

The description is appropriately sized for the tool's complexity. It is front-loaded with the core purpose, then logically flows into input/output, criteria details, per-candidate output, ethical warning, and processing characteristics. Every sentence earns its place without redundancy.

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 tool's complexity (nested objects, 5 parameters, output schema exists), the description covers all essential aspects: inputs, output structure, criteria definitions, diversity signal limitation, and processing mode. Even though an output schema exists, it goes further to explain score ranges and flags, making it fully complete for selection and invocation.

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 description coverage is 100%, so the baseline is 3. The description adds meaning beyond the schema by explaining what each weighting criterion represents (e.g., skills_match as 'tech stack and soft skills extracted from JD vs resume') and clarifying the candidate limits (1-50), which enriches the parameter understanding.

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 states a specific verb and resource: 'AI-powered candidate screening and ranking' and details the exact input (job description + 1-50 resumes) and output (ranked shortlist with score breakdowns). It distinguishes from siblings by covering all five weighted criteria and per-candidate outputs, making its scope clear even without 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 Guidelines4/5

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

The description provides clear context: it is for recruiters, hiring managers, ATS providers and recruitment AI agents, and explicitly mentions the input range (1-50 resumes). However, it does not explicitly state when to avoid using it or mention alternative sibling tools, so it stops short of full exclusion guidance.

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

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TDQS

C2.4/5.0
Disambiguation1/5

Over 50 tools share the identical template 'Gapup agent-payable C-suite expertise' with similar French descriptions and reference cases, making their boundaries indistinguishable. Clusters like competitor_intel, competitive_deep_dive, competitor_moves, competitor_profiles, competitor_pricing_radar, competitor_pricing_scrape, and competitor_recommendations heavily overlap in purpose.

Naming Consistency1/5

Names are chaotic: mix of French and English, snake_case and camelCase, verb_noun, noun, and adjective forms with no uniform pattern. Examples like 'bp_narratif', 'content_enrichment', 'ai_governance_full_report_async', and 'job_result' show no coherent naming convention.

Tool Count1/5

271 tools is far beyond any reasonable MCP server scope, creating an overwhelming selection burden for agents. This count vastly exceeds the 25+ threshold for 'too many' and makes navigation impractical.

Completeness2/5

While the server covers many business domains, it lacks lifecycle operations (e.g., no update/delete tools for the deliverables it generates) and the input specifications are vague ('documented case fields' without documentation), creating functional dead ends. The sheer breadth does not compensate for these gaps.

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