gapup-mcp

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

Name	Required	Description
`async`	No	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 timeouts.
`candidates`	Yes	Array of candidate objects. Maximum 50.
`role_country`	No	Optional ISO 2-letter country code for regional context (informational).
`job_description`	Yes	Full text or summary of the job description and role requirements.
`criteria_weights`	No	Optional weighting per criterion. Default: skills=0.4, experience=0.2, education=0.1, progression=0.15, culture=0.15.

Output Schema

TableJSON Schema

Name	Required	Description	Default
`status`	Yes
`sources`	Yes
`nice_to_have`	Yes
`quality_score`	Yes
`required_skills`	Yes
`candidates_ranked`	Yes
`diversity_signals`	No
`shortlist_recommended`	Yes
`job_description_summary`	Yes

TDQS

A4.8/5.0

Behavior5/5

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

The description goes beyond annotations by explicitly stating that all processing is local, privacy-preserving, instant, and that diversity signals are first-name proxies only with a mandatory ethical warning. It also describes the scoring breakdown per criterion and the output features (red flags, green flags, interview questions). This adds significant behavioral context that annotations alone do not provide.

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 information-dense yet well-structured. It front-loads the purpose, then lists criteria, output format, and caveats. Every sentence adds value without redundancy, achieving conciseness despite the complexity of the tool.

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 (5 parameters, nested objects, output schema), the description covers all necessary aspects: input requirements, processing details, scoring criteria, output fields (including ethical warnings), and performance characteristics. It leaves no obvious gaps for an agent to misunderstand usage or expectations.

Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.

Parameters5/5

Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?

With 100% schema description coverage, the baseline is 3, but the description adds substantial meaning beyond the schema by explaining the five weighted criteria in detail (e.g., skills_match extracts tech stack and soft skills, experience_match compares years to seniority level). This extra context helps the agent understand how to effectively set criteria_weights and interpret results.

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 clearly states the tool's purpose: AI-powered candidate screening and ranking that ingests a job description and 1-50 resumes to return a ranked shortlist with score breakdowns across five weighted criteria. It uses specific verbs and resources ('screening and ranking', 'ingests', 'returning') and provides detailed criteria, effectively distinguishing it from sibling tools like 'talent_intelligence' or 'recruiting_architect'.

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 implicitly defines usage by detailing the tool's input and output, making it clear that it is intended for screening and ranking candidates. However, it does not explicitly state when to use this tool versus alternatives, nor does it mention exclusion criteria or prerequisites.

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

C2.5/5.0

Disambiguation2/5

With 271 tools, many have overlapping purposes (e.g., multiple competitor intel tools, multiple financial modelers, multiple ESG auditors). Detailed descriptions help slightly, but the sheer volume creates confusion. Agents would struggle to select the right tool among many similar options.

Naming Consistency1/5

Tool names are wildly inconsistent: mix of English and French, snake_case and short phrases, some very generic (process, run, execute equivalents). No discernible naming convention (e.g., abm_architect vs. boundary_control vs. bp_narratif). This makes it hard to predict tool names.

Tool Count1/5

271 tools is far beyond typical well-scoped servers (3-15). This indicates an unfocused, over-bloated tool surface. Even for a general business intelligence server, this number is excessive and violates the principle of each tool earning its place.

Completeness2/5

Despite the large count, coverage feels scattered. Some domains (e.g., content, competitive intel) have many tools, while others (e.g., supply chain, HR) have gaps. The set lacks a coherent scope; it seems like a dump of many separate tool collections rather than a complete, curated surface.

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