Purchase paid extras for an existing job posting: sticky ($299), newsletter ($99). Returns a Stripe checkout URL. Requires employer authentication.
| Name | Required | Description | Default |
|---|---|---|---|
| extras | Yes | Extras to purchase: 'sticky' ($299 pin to top), 'newsletter' ($99 weekly email feature) | |
| job_slug | Yes | Job slug to add extras to |
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
With no annotations, the description carries the full burden. It states the action (purchase), the return (checkout URL), and a prerequisite (employer auth). It does not disclose whether the job posting is immediately modified or if payment is processed separately, but the 'checkout URL' implies a deferred flow. This is a reasonable level of transparency for a purchase tool.
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?
A single, concise sentence packs the purpose, specific options, prices, return value, and auth requirement. No wasted words; all essential information is front-loaded and efficiently structured.
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?
The description covers the core aspects: what it does, the return type, auth requirement, and specifics. However, it omits details like idempotency, error handling, or whether the checkout URL is temporary. For a purchase operation, this is slightly incomplete but still adequate for an agent to call it correctly.
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% with both parameters already described, including prices for extras. The description repeats the prices and purpose but adds no new semantic detail beyond the schema. This meets the baseline of 3 for high schema coverage; it does not substantially enhance parameter understanding.
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?
Description clearly states the verb 'Purchase', the resource 'paid extras for an existing job posting', and the specific extras (sticky $299, newsletter $99). It also mentions the return value (Stripe checkout URL), distinguishing it from job creation or status tools. This is unambiguous and specific.
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 when to use it — when you need to add paid extras to an existing job. It also notes the authentication requirement. However, it does not explicitly mention alternatives or when not to use it (e.g., if the job is not yet posted). This leaves some inference, but the context is clear enough.
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.