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Search job titles

search_job_titles
Read-onlyIdempotent

Autocomplete ApplyAll job titles so the user can pick a canonical title.

Tool call template

Replace each placeholder with a value matching the input schema. Only required parameters are included; add optional parameters as needed.

{
  "id": 1,
  "jsonrpc": "2.0",
  "method": "tools/call",
  "params": {
    "arguments": {
      "query": "<query>"
    },
    "name": "search_job_titles"
  }
}

Input Schema

NameRequiredDescriptionDefault
limitNo1–100, default 20.
queryYesPartial title.

Schema Changelog

Changes observed during successful MCP inspections.

  1. First observed

TDQS

A4/5.0
Behavior3/5

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

Annotations already declare readOnlyHint=true, idempotentHint=true, and destructiveHint=false, covering safety. The description adds that it 'autocompletes' and returns canonical titles, which is useful context. However, it does not disclose the return format, ordering, or pagination behavior. Since annotations cover the safety profile, the description's contribution is minimal but sufficient, warranting a 3.

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 a single, efficient sentence that front-loads the core purpose. Every word adds value, with no redundancy or filler. It is appropriately concise for a simple tool.

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?

For a simple autocomplete tool with two well-schema-described parameters and safety annotations, the description is nearly complete. It does not mention return value details, but the purpose ('autocomplete' and 'canonical title') implies a list of suggestions. Given the low complexity and high schema/annotation coverage, the description is sufficient for an agent to invoke correctly. Slight gap on output format, but not critical.

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% (query: 'Partial title.', limit: '1–100, default 20.'). The description does not add parameter-specific details beyond what the schema already provides; it merely implies the query is for autocomplete. According to the baseline for high schema coverage, a score of 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?

The description states a specific verb ('Autocomplete') and resource ('ApplyAll job titles'), and clarifies the output purpose: 'so the user can pick a canonical title.' This distinguishes it from sibling search_jobs (which searches job listings) and search_locations (locations), as it targets canonical job title suggestions. The purpose is unambiguous and specific.

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?

The description indicates it is for autocomplete, implying it should be used when a user is typing a partial job title and needs suggestions. It does not explicitly name alternatives or exclusions (e.g., 'use search_jobs for full listings'), but the context is clear enough that an agent can infer the appropriate use case without confusion. Lacks explicit when-not guidance.

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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