fetch_job_specFetches and extracts the job description text from a public job-posting URL, ready to feed into score_cv or tailor_cv_to_role. Cheap, no LLM.
| Name | Required | Description | Default |
|---|---|---|---|
| url | Yes | A public https:// job posting URL. |
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
Annotations already declare readOnlyHint=true and openWorldHint=true. The description adds value by stating 'Cheap, no LLM' (cost/performance) and that it extracts text, which goes beyond the structured fields. 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?
Two sentences, front-loaded with the core purpose, followed by usage context and cost. Every sentence is informative and no wasted 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 tool with one parameter, no output schema, and no nested objects, the description covers purpose, usage, cost, and output sufficiently. No gaps given the complexity.
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 the parameter description in the schema already specifies 'A public https:// job posting URL.' The tool description does not add additional semantics beyond that, meeting the baseline.
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?
The description clearly states the verb ('Fetches and extracts') and the resource ('job description text from a public job-posting URL'). It also mentions downstream tools (score_cv, tailor_cv_to_role), distinguishing it from siblings like ats_lint or find_roles.
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?
The description indicates when to use this tool ('ready to feed into score_cv or tailor_cv_to_role') and notes it is 'Cheap, no LLM'. It does not explicitly state when not to use it or provide alternatives, but the context is clear.
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.