Get full details for a specific job including description, requirements, salary, and application link. Use the company_slug and job_slug from job listings.
| Name | Required | Description | Default |
|---|---|---|---|
| job_slug | Yes | Job slug (from the job listing URL) | |
| company_slug | Yes | Company slug (from the job listing URL or company page) |
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
With no annotations, the description carries the burden of behavioral disclosure. It does reveal that the tool returns details including description, requirements, salary, and application link, which is useful. However, it does not mention error behavior, authentication needs, or whether the operation is strictly read-only beyond the verb 'Get'.
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 tight sentences deliver the core purpose and the parameter sourcing guidance without any redundant filler. The main action is front-loaded.
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 two-parameter getter with no output schema, the description covers the main behavior, the output fields, and how to obtain the required slugs. It is missing only minor details like not-found handling, which are not critical for correct invocation.
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 telling the agent where to source the slugs ('from job listings'), which is contextual guidance beyond the schema's basic field descriptions.
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 retrieves full details for a specific job and lists key content fields (description, requirements, salary, application link). However, it does not explicitly distinguish itself from similar siblings like show_company_job, so it stops short of a 5.
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 to use company_slug and job_slug from job listings implies when the tool is appropriate, but there is no explicit guidance on when to prefer this tool over alternatives like get_jobs or list_company_jobs. The usage context is present but not fully developed.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
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Each tool targets a distinct resource and action: job posting vs job browsing vs job management vs company management vs talent search vs profile editing vs messaging vs application tracking. Even similar tools like get_companies and search_companies are clearly differentiated by purpose and parameters. Overlapping concepts (e.g., post_job_public vs create_company_job) have explicit differences in authentication and cost.
All tools use snake_case and follow a verb-first pattern (add_, get_, create_, update_, delete_, search_, list_, send_, etc.). There are minor deviations like 'show_company_job' instead of 'get_company_job' and 'mark_message_read' which is a verb+noun+adjective, but the overall style is consistent and predictable across the 41 tools.
With 41 tools, this is well into the 'too many' range (25+). While the breadth reflects a comprehensive jobs platform, the number is excessive for an agent to efficiently navigate. Many tools could be consolidated (e.g., profile management could merge add_education/add_experience/update_profile, or company perks could be combined with profile updates). The tool count detracts from usability.
The tool set covers the full lifecycle: job posting (create, update, delete, list), job discovery (browse, search, related), company management (profile, perks, tech stack), talent search and messaging, application tracking (save, get, remove, update status), and data analytics (salary, statistics). Minor gaps exist—no delete/update for education or experience, no explicit 'close job' action—but these are edge cases and agents can work around them.