Find jobs similar to a specific job, based on skills, location, and category. Use after viewing a job to suggest alternatives.
| Name | Required | Description | Default |
|---|---|---|---|
| job_slug | Yes | Job slug of the original job | |
| company_slug | Yes | Company slug of the original job |
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
With no annotations provided, the description carries the full burden. It discloses the basis of similarity (skills, location, category) but does not explain the return format, ordering, pagination, or any limitations. For a read-oriented tool this is adequate but shallow; an agent might not know whether results include full job objects or summaries.
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 zero waste. The primary purpose is stated first, followed by a usage hint. Every word contributes, and the description is immediately scannable.
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?
This is a simple tool with two required parameters and no output schema. The description includes the recommendation context and the matching criteria, but does not mention what the response looks like (e.g., a list of job objects) or any limits. Given the sibling tools like get_jobs, an agent could infer the return shape, but the description alone is slightly incomplete for a tool with no output schema.
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 100% coverage; both parameters have descriptions that state they are slugs of the original job. The tool description does not add extra meaning beyond this, but the schema descriptions are already sufficient. Baseline 3 is appropriate since the description adds no redundant detail yet the parameters are self-explanatory.
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 finds jobs similar to a specific job, and specifies the criteria (skills, location, category). It distinguishes from generic listing or search tools by focusing on similarity. The added context 'Use after viewing a job to suggest alternatives' further clarifies its purpose, making it 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 explicitly says 'Use after viewing a job to suggest alternatives,' giving a clear trigger condition for when this tool is appropriate. While it doesn't name specific alternative tools, the shown usage is sufficiently targeted to guide an agent. It could be stronger with an explicit 'instead of search_jobs' but the intent is clear.
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.