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CoverCraft AI by Ambuj Kumar Tripathi

jd_analyzer

Read-onlyIdempotent

Parse target Job Description and match required skills against candidate experience with evidence classification and gap detection.

Input Schema

NameRequiredDescriptionDefault
jd_textYesJob description text
resume_textYesCandidate resume text

Output Schema

NameRequiredDescriptionDefault
chart_dataYesCategorical breakdown of strong, partial, transferable, and missing skills
role_titleYesIdentified target job title
overall_matchYesDeterministic overall match percentage (0-100)
required_skillsYesExtracted required skills with evidence match

Schema Changelog

Changes observed during successful MCP inspections.

  1. First observed

TDQS

B3.2/5.0
Behavior3/5

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

Annotations already declare readOnly, idempotent, non-destructive and closed-world, so the safety profile is fully covered. The description adds useful behavioral detail about the processing pipeline (evidence classification, gap detection) but says nothing about determinism, limits, or handling of long inputs beyond what annotations provide.

Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.

Conciseness4/5

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

A single dense sentence that front-loads the core action and appends the analysis steps. Nothing is wasted, though the compound clause structure packs four concepts together and could be marginally clearer.

Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.

Completeness4/5

Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?

With an output schema present, the description need not explain return values, and annotations cover the safety profile. The description conveys the analysis workflow adequately; only the specifics of what 'evidence classification' produces are left implicit.

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

Parameters3/5

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

Schema description coverage is 100% for both parameters, so the schema already documents jd_text and resume_text. The description references 'Job Description' and 'candidate experience' but adds no format, size, or content expectations beyond the schema. Baseline 3 applies.

Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.

Purpose4/5

Does the description clearly state what the tool does and how it differs from similar tools?

States specific verbs and resources: parse a job description, match required skills against candidate experience, classify evidence, detect gaps. This is clearly distinguishable from siblings like evidence_validator or ats_readiness. It stops short of explicitly contrasting itself with those siblings, so not a 5.

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

Usage Guidelines2/5

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

The description implies the usage context (you supply a JD and a resume) but never states when to choose this tool over evidence_validator or ats_readiness, nor any prerequisites or exclusions. No when-not guidance at all.

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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