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XJTLUmedia

AI HR Management Toolkit

inspect_pipeline

Read-only

Run a 5-node atomic deconstruction pipeline on resume text to analyze processing stages, confidence scores, entity classification, and data quality. Understand parser behavior and identify low-confidence areas.

Instructions

Run the full 5-node atomic deconstruction pipeline (Ingestion → Sanitization → Tokenization → Classification → Serialization) on resume text. Returns stage-by-stage metrics, confidence scores, entity classification with disambiguation, data quality assessment, and assumption audit. Use this to understand HOW the parser processes a resume and WHERE confidence is low.

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
resumeTextYesThe raw text content of a resume
Behavior4/5

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

Annotations already indicate readOnlyHint=true (safe read) and openWorldHint=false (closed world). Description adds details about the pipeline stages and return values, complementing annotations well without contradiction.

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?

Two sentences with front-loaded pipeline stages and return values. No redundant information; every sentence adds value.

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 pipeline inspection tool with one parameter and no output schema, the description thoroughly explains the pipeline stages and the types of metrics returned, making it complete for selection and invocation.

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?

Only one parameter with 100% schema coverage; the description doesn't add meaning beyond the schema's description of 'raw text content of a resume.' Baseline 3 is appropriate.

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 the tool runs a specific 5-node pipeline on resume text and lists what it returns (metrics, confidence, etc.). It distinguishes from siblings by focusing on the internal processing steps rather than just extraction or analysis.

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?

Description explicitly says 'Use this to understand HOW the parser processes a resume and WHERE confidence is low,' providing clear guidance on when to use. It does not mention when not to use or alternatives, but the 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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