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Job Zone Classification

classify_job_zone
Read-onlyIdempotent

RChilli's Job Zone Classification tool reads the job profile from a resume or job description and returns its ONET Job Zone — one of five standardized levels ranging from Zone 1 (little or no preparation required) through Zone 2 (some preparation), Zone 3 (medium preparation), Zone 4 (considerable preparation), to Zone 5 (extensive preparation required) — based on the education, experience, and training criteria defined by ONET. The returned Job Zone level enables downstream workflows such as candidate-to-role fit filtering, compensation benchmarking, over/under-qualification flagging, and job architecture standardization without any manual O*NET lookup.

Input Schema

NameRequiredDescriptionDefault
filenameNoOriginal filename with extension (e.g. 'resume.pdf'). Defaults to 'resume.txt' if omitted.
json_textNoPre-parsed resume JSON string. When provided, re-parsing is skipped.
requesttypeNoType of document — 'Resume' (default) or 'JD'.Resume
resume_textNoPlain text content of the resume (or JD). The server encodes it internally.

Schema Changelog

Changes observed during successful MCP inspections.

  1. Added

TDQS

A3.7/5.0
Behavior4/5

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

Annotations already declare read-only, idempotent, non-destructive, open-world behavior, so the safety profile is covered. The description adds useful behavioral context by explaining that the tool returns one of five standardized O*NET Job Zones based on education, experience, and training criteria, though it does not discuss error handling or source-document parsing limits.

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 front-loaded with the core action and return value, then explains the five Job Zones and downstream value. It is somewhat long due to the full enumeration of zones, but the information is relevant and structured rather than repetitive.

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?

There is no output schema, so the description appropriately explains that the tool returns a standardized O*NET Job Zone level and what that enables. Combined with the fully described input schema and safety annotations, it is complete enough for an agent to invoke the tool correctly.

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%, so the input schema already documents all four parameters, including filename, json_text, requesttype, and resume_text. The description does not add parameter-level semantics beyond what the schema provides, which is acceptable but not enriching.

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 states a specific verb and resource: it reads a resume/JD job profile and returns an O*NET Job Zone. It clearly explains the five-level output and the criteria behind it, but it does not explicitly distinguish this tool from similar siblings such as lookup_job_profile or autocomplete_job_profile.

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

Usage Guidelines3/5

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

The description lists downstream use cases like fit filtering, compensation benchmarking, and qualification flagging, which implies when the tool is useful. However, it does not give explicit conditions for choosing this tool over sibling tools or state any exclusions 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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