get_jobFetch full job posting by ID or slug: title, description, requirements, salary, workplace, tags, apply URL. Use after search_jobs to read a specific listing.
| Name | Required | Description | Default |
|---|---|---|---|
| id | Yes | Job ID (UUID) or slug |
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
No annotations are provided, so the description must convey behavioral context. It describes the content delivered but does not explicitly state whether the operation is read-only, requires authentication, or how errors are handled, leaving some transparency gaps.
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 concise sentences with no redundancy: the first lists the returned data, the second gives usage guidance. Every word adds value.
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 single-parameter fetch tool without an output schema, the description sufficiently covers what data will be returned and the recommended usage context. No critical information is missing.
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?
The input schema already fully documents the single parameter 'id' as a UUID or slug (100% coverage). The description adds no new parameter semantics beyond restating that it accepts an ID or slug, so it meets 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 explicitly states 'Fetch full job posting by ID or slug' and enumerates the fields returned, clearly distinguishing it from sibling tools like search_jobs (which searches) and get_company (which fetches company info).
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 instruction 'Use after search_jobs to read a specific listing' explicitly tells the agent when to invoke this tool relative to its primary sibling, establishing a clear workflow.
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.
Most tools have clear, distinct purposes (e.g., search_jobs vs get_job vs get_similar_jobs). The main ambiguity is between match_jobs and analyze_application_readiness, both of which assess candidate-job fit, though one ranks multiple jobs and the other evaluates readiness for a specific role. The application flow steps are well-separated.
All tool names consistently follow the snake_case verb_noun pattern (e.g., get_company, list_companies, apply_to_job). Even longer names like analyze_application_readiness and compile_job_specific_resume adhere to this convention, with no mixed casing or inconsistent verb styles.
With 20 tools, the server is on the heavier side for a job board, though the breadth of features (search, company info, salary, application, interview tracking, and product sales) partially justifies the count. Some redundancy exists (e.g., get_trending_companies vs list_companies, get_stats vs get_salary_data), making the set feel slightly bloated.
The toolset covers core job search and application workflows, but there is no way to list or track submitted applications, view application status, or withdraw an application. Post-application features are limited to interview outcomes, leaving obvious lifecycle gaps for a candidate-facing job platform.