employer.get_jobFetch a single employer job record by id. Returns NOT_FOUND for missing jobs and for jobs owned by another employer (no existence leak).
| Name | Required | Description | Default |
|---|---|---|---|
| jobId | Yes | ||
| apiKey | No | Optional WorkorAI MCP key for in-session authentication when the MCP client was initialized anonymously. |
| Name | Required | Description | Default |
|---|---|---|---|
| ok | No | ||
| job | No |
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
With no annotations provided, the description carries the full burden for behavioral transparency. It discloses critical behavior: returns NOT_FOUND for missing jobs and for jobs owned by another employer, preventing existence leaks. This goes beyond a simple fetch description. However, it doesn't cover authentication requirements or rate limits, which are less critical for a read operation.
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 with no unnecessary words. The first sentence states the core purpose, and the second adds important behavioral context about error handling. Every sentence earns its place.
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 that there is an output schema (mentioned but not provided), the description is likely sufficient for a simple fetch tool. It explains the return value behavior for errors. However, it could be more complete by noting that the output schema will contain the job details, but since output schema exists, it's not necessary. The tool is simple enough that this description is adequate.
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 schema has 50% parameter description coverage (apiKey has a description, jobId does not). The tool description mentions 'by id' but does not add any specific details about jobId's format, constraints, or how it relates to the employer context. It relies on the schema for the apiKey parameter. While acceptable, it doesn't fully compensate for the missing jobId 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 clearly states the verb 'Fetch', the resource 'single employer job record', and the identifier 'by id'. It also distinguishes from sibling tools like employer.list_jobs by specifying it retrieves a single record, and addresses error handling with 'Returns NOT_FOUND for missing jobs and for jobs owned by another employer (no existence leak)', which adds clarity.
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 implies usage for retrieving a specific job record, and the error handling notes help the agent understand when to expect NOT_FOUND. However, it does not explicitly mention when to use this versus employer.list_jobs or candidate.get_job, but the context of sibling tools makes it clear enough.
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.
Every tool in the candidate and employer sets targets a distinct action or resource with no ambiguity. Tools like `search_candidates_by_query` and `search_candidates_for_job` have clearly different purposes, and all other tools perform unique operations.
All tools follow a consistent `domain.action` pattern with snake_case action names. The naming is uniform across both candidate and employer tools, using standard verbs like get, list, create, update, delete, search, set, etc.
29 tools cover two distinct user roles (candidate and employer) with separate workflows. While above the typical 3-15 range, each tool serves a specific purpose and the count is justified for a hiring platform's API surface.
The tool surface provides comprehensive CRUD and lifecycle operations for jobs, applications, invitations, and candidate searches for both roles. Minor gaps like candidate profile update tools are likely handled outside the MCP server, so the set feels nearly complete.