find_rolesSearches live job listings for a role query (e.g. "frontend engineer London"). Cheap, no LLM — returns a short list of matching roles with company and link.
| Name | Required | Description | Default |
|---|---|---|---|
| query | Yes | A role query, e.g. "frontend engineer London". |
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
Adds useful behavioral context beyond annotations: 'Cheap, no LLM' and 'returns a short list with company and link'. No contradictions.
Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.
Is the description appropriately sized, front-loaded, and free of redundancy?
Single sentence, front-loaded with purpose, no redundant words.
Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.
Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?
For a simple search tool with one parameter and annotations, the description provides purpose, example, and output nature. Minor gaps (pagination, error handling) are acceptable.
Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.
Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?
Schema coverage is 100% and schema description already includes the example; description adds output info but not parameter-specific improvements.
Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.
Does the description clearly state what the tool does and how it differs from similar tools?
Description clearly states 'Searches live job listings for a role query' with a concrete example, and the tool is distinct from siblings which handle linting, CV scoring, etc.
Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.
Does the description explain when to use this tool, when not to, or what alternatives exist?
Mentions 'Cheap, no LLM' implying lightweight usage, but doesn't explicitly state when to prefer this over fetch_job_spec or other siblings.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
Add one secure layer between your agents and this server.
Each tool has a distinct purpose: ATS linting, job spec fetching, role searching, CV scoring, and CV tailoring. No two tools overlap in functionality.
Most tools follow a verb_noun pattern (fetch_job_spec, find_roles, score_cv, tailor_cv_to_role), but ats_lint starts with a noun, breaking consistency.
Five tools is a well-scoped set for the job search and CV optimization domain, covering key operations without being too few or excessive.
The tools form a complete workflow: find roles → fetch specs → score CV → tailor CV, with linting as a quality check. No obvious gaps for the intended use case.