Get detailed information about a specific job listing/posting by its job listing ID (not application ID). Use this to view the full job posting details including description, salary, skills, and company info. For job application details, use get_application instead.
| Name | Required | Description | Default |
|---|---|---|---|
| id | Yes | The job ID |
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
No annotations are provided, so the description must carry the behavioral disclosure burden. It does describe the operation as retrieving detailed job information and lists the types of data returned (description, salary, skills, company info), which implies a read-only operation. However, it does not explicitly state that the operation is non-destructive, nor does it address error cases, authentication requirements, or the full return structure. This is adequate for a simple get-by-id tool but leaves some behavioral aspects implicit.
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 concise sentences. The first sentence states the action and scope, the second provides usage guidance and an alternative. Every sentence adds value with no redundancy.
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 get-by-id tool, the description covers the purpose, the distinguishing parameter semantics, and the types of information returned in the absence of an output schema. It does not cover error handling or access prerequisites, but given the low complexity and the explicit mention of the return content, it is nearly complete. A small gap remains in not describing the exact response structure.
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 single parameter 'id' is described in the schema as 'The job ID'. The description adds meaningful context by clarifying it is the 'job listing ID (not application ID)', which is critical for correct usage and prevents confusion with get_application's ID. This goes beyond the schema's terse description.
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 uses a specific verb ('Get') and resource ('detailed information about a specific job listing/posting'), explicitly scopes it by job listing ID (not application ID), and differentiates from get_application. It also lists the key fields returned, making the purpose unmistakable.
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 clearly states when to use this tool: to view full job posting details. It explicitly directs users to get_application for application details, providing an alternative. However, it doesn't mention other relevant alternatives like search_jobs for finding jobs without an ID, so it is clear but not exhaustive.
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 targets a distinct resource and action. Tools like get_job vs get_application vs get_job_hunt are clearly separated, and match_jobs vs search_jobs are well-differentiated by saved vs explicit filters. No two tools appear to do the same thing.
All tool names follow a consistent verb_noun pattern in snake_case (e.g., create_job_hunt, list_applications, update_salary). Even longer names like add_job_to_applications maintain the convention with clear, predictable structure.
With 35 tools, the server exceeds the 25+ threshold that indicates an overly large surface. While the breadth covers a comprehensive job search workflow, the number is likely overwhelming and could be consolidated without losing functionality.
The tool set covers the full job hunt lifecycle: creating hunts, searching/matching jobs, applying, tracking applications, managing resumes (including AI-generated versions), outreach, interviews, profile, and compensation. There are no obvious dead ends; update and delete operations are available where needed.
