get_similar_jobsGiven a job ID or slug, return active jobs that match on tag overlap, same workplace, level, and job type. Ranked by similarity score (tag overlap weighted 3x). Use after search_jobs to surface nearby roles — matches the /jobs/{id}/similar REST endpoint.
| Name | Required | Description | Default |
|---|---|---|---|
| id | Yes | Job ID (UUID) or slug — same identifier get_job accepts | |
| limit | No | Max results (default 5, max 20) |
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
With no annotations provided, the description carries the full burden of behavioral disclosure. It reveals the matching criteria (tag overlap, workplace, level, job type), ranking mechanism (similarity score, tag overlap weighted 3x), and the 'active jobs' filter. It does not detail response format or edge cases, but for a read-only tool, this is substantial transparency.
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?
The description is two sentences, front-loaded with the core function, and every phrase conveys meaning. It efficiently includes the matching criteria, ranking weighting, usage context, and endpoint mapping without redundancy or filler.
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?
Given the tool's simplicity (2 params, no output schema, no annotations), the description adequately covers purpose, usage, behavior, and parameters. It could mention pagination or empty results, but the provided details are sufficient for an agent to select and invoke the tool correctly in most scenarios.
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%, so the baseline is 3. The description adds value by noting that the id parameter is 'the same identifier get_job accepts,' which clarifies cross-tool compatibility, and it mentions the default limit of 5, which the schema does not specify. This enriches the parameter semantics beyond the schema alone.
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 tool's function: 'Given a job ID or slug, return active jobs that match on tag overlap, same workplace, level, and job type.' It specifies the verb 'return' and the resource, distinguishing it from siblings like search_jobs (which searches broadly) and get_job (which returns a single job). The mention of the REST endpoint further clarifies its role.
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 provides explicit context: 'Use after search_jobs to surface nearby roles.' This tells the agent when to invoke the tool relative to a common workflow. It does not explicitly list exclusions or alternative tools, but the usage guidance is clear and actionable, which places it above a generic description.
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.