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XJTLUmedia

AI HR Management Toolkit

analyze_resume_comprehensive

Read-only

Run a full pipeline analysis on a resume: ingest, sanitize, tokenize, classify, and serialize. Returns skill estimates, experience timeline, career analysis, and optional job match scores.

Instructions

Run a comprehensive algorithmic analysis on a resume in a single call. Accepts raw text or a file (base64-encoded PDF/DOCX/TXT/MD or URL). Runs the full 5-node pipeline (Ingestion → Sanitization → Tokenization → Classification → Serialization) and returns: pipeline confidence scores, classified entities by type, categorized skills with proficiency estimates, structured experience timeline, career analysis, contact info, metrics/achievements, section quality assessment, and data quality scores. Optionally matches against a job description. This is a one-call alternative to chaining parse_resume + inspect_pipeline + classify_entities + extract_skills_structured + extract_experience_structured + compute_similarity.

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
contentNoBase64-encoded file content or URL. Use with fileType. Ignored if resumeText is provided.
fileTypeNoFile type when using content parameter
resumeTextNoRaw resume text. Provide either resumeText OR (content + fileType), not both.
jobDescriptionNoOptional job description to compute similarity and skill gap analysis
requiredSkillsNoOptional required skills to check against
Behavior4/5

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

Annotations already declare readOnlyHint and openWorldHint. The description adds value by detailing the 5-node pipeline and what outputs are returned (confidence scores, entities, skills, timeline, etc.), without contradicting annotations.

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?

The description is comprehensive and well-structured but slightly verbose. It front-loads the purpose and then lists details, though some sentences could be tightened without losing clarity.

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?

For a complex tool with 5 parameters, no output schema, and a pipeline, the description covers inputs, pipeline steps, outputs, and usage context (alternative to chaining). It leaves no critical gaps for an AI agent.

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?

Schema coverage is 100%, and the description adds significant meaning: explains each parameter (content, fileType, resumeText, jobDescription, requiredSkills) including the mutual exclusivity of content+fileType vs resumeText, and optional nature of job matching parameters.

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?

Description clearly states it runs comprehensive algorithmic analysis on a resume, lists pipeline stages and returns, and distinguishes from sibling tools by noting it's a one-call alternative to chaining multiple specific tools.

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?

Explicitly states this is a one-call alternative to chaining other tools, providing clear guidance on when to use it. Input options are explained (raw text or file with fileType). No explicit when-not-to-use, but context is clear.

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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