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Job Description Data Extraction

extract_job_data
Read-onlyIdempotent

Extracts and converts job descriptions into structured hiring data including job title, required skills, preferred skills, responsibilities, experience, education, and taxonomy-normalized role requirements for recruitment automation and candidate matching.

Input Schema

NameRequiredDescriptionDefault
jd_textNoComplete, verbatim plain-text of the job description / job posting — every line as in the original. Do NOT summarise or base64-encode; pass raw text.

Output Schema

NameRequiredDescriptionDefault
dataYesTool-specific result payload. The exact fields depend on which tool produced this result.
metaYesRequest metadata: unique trace ID and server-side processing latency.
successNoAlways ``true`` for a successful result. Check this field first to distinguish success from error envelopes.

Schema Changelog

Changes observed during successful MCP inspections.

  1. Added

TDQS

B3.2/5.0
Behavior3/5

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

Annotations already declare readOnlyHint, idempotentHint, openWorldHint, and non-destructive behavior, so the safety profile is fully covered without the description. The description adds the notion that role requirements are 'taxonomy-normalized', which hints at transformation behavior, but says nothing about input constraints, limits, or failure modes; and the field list largely duplicates what the output schema provides.

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?

It is a single front-loaded sentence with the verb and resource first. The long enumeration of extracted fields is slightly padded, but every clause is on-topic and there is no filler.

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 return values need not be explained, and the lone input parameter is fully documented in the schema. The description supplies the domain framing (hiring data, taxonomy normalization) needed to place the tool, leaving only selection guidance against siblings unaddressed.

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?

There is a single parameter with 100% schema description coverage, including an explicit warning to pass verbatim raw text rather than summaries or base64. With coverage this high the schema carries the semantics, so the description adds nothing param-specific and the baseline of 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?

The description uses a specific verb+resource ('Extracts and converts job descriptions into structured hiring data') and enumerates the output fields, so the agent knows exactly what it produces. The resource name distinguishes it from siblings like extract_resume_data, but no sibling is named explicitly, keeping it just short of 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 only usage signal is a generic purpose phrase ('for recruitment automation and candidate matching'), which describes downstream consumers rather than when to call this tool. There is no guidance on when to prefer it over extract_resume_data, map_job_to_skills, or generate_jd, and no prerequisites or exclusions are given.

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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