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by io.github.worklittle
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Jobs · Get Job Keywords

get_job_keywords
Read-only

Jobs — Get skill and technology keywords from live Worklittle job data. Pass job_id for one posting. If the user asks what keywords show up for a role (resume tailoring, 'data scientist jobs', applications) and you do not have a job_id, pass query instead (optional location). Returns keywords plus keyword_count. Do not require a prior search when the question is about a role in general.

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
queryNoRole or title phrase when aggregating common keywords across live jobs (e.g. data scientist).
job_idNoJob ID from search_jobs when keywords are for one listing.
locationNoOptional location substring when aggregating by query.

Output Schema

TableJSON Schema
NameRequiredDescriptionDefault

No arguments

Schema Changelog

Changes observed during successful MCP inspections.

  1. Changed4 schema fields changed
    • changedInput schema / properties / job_id / description
      Previous value: -"The job ID to fetch keywords for."New value: +"Job ID from search_jobs when keywords are for one listing."
    • addedInput schema / properties / location
      Added value: +{
+  "description": "Optional location substring when aggregating by query.",
+  "type": "string"
+}
    • addedInput schema / properties / query
      Added value: +{
+  "description": "Role or title phrase when aggregating common keywords across live jobs (e.g. data scientist).",
+  "type": "string"
+}
    • removedInput schema / required
      Removed value: -[
-  "job_id"
-]
  2. First observed

TDQS

A4.7/5.0
Behavior4/5

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

Annotations already indicate a safe, read-only, open-world operation. The description adds useful behavioral context beyond the annotations: it deals with 'live' job data, returns 'keywords plus keyword_count', and explicitly states that a prior search is not required for general role questions. This gives agents a clear picture of the tool's operating assumptions.

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?

The description is compact and every sentence earns its place: it states the core purpose, gives parameter selection guidance, notes return fields, and adds a critical open-world caveat. It is front-loaded with the tool's function and avoids unnecessary examples or filler.

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?

With an output schema present, 100% parameter schema coverage, and clear annotations, the description fully equips an agent to invoke the tool correctly. It covers the main decision (job_id vs query), optional location usage, return contents, and the important instruction not to force a prior search. Nothing essential is missing for this read-only, straightforward tool.

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

Parameters4/5

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

Although the schema already covers all three parameters (100% coverage), the description adds decision-making context: job_id is for one listing, query is for aggregating across roles, and location is optional for the query path. This goes beyond raw schema definitions by connecting each parameter to the use case that should trigger it.

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?

The description clearly identifies the action ('Get') and the resource ('skill and technology keywords from live Worklittle job data'), and distinguishes this tool from similarly named siblings like get_job_details by focusing on keyword extraction rather than job details. It also clarifies that it can operate on either a single job_id or an aggregated query, leaving no ambiguity about what the tool returns.

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

Usage Guidelines5/5

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

The description explicitly states when to pass job_id vs query, including the condition 'if the user asks what keywords show up for a role ... and you do not have a job_id'. It also provides an explicit exclusion by saying 'Do not require a prior search when the question is about a role in general', which directly guides tool selection behavior.

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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