Get full details for a candidate including bio, all experiences, education, tech stack, social links, and more. Use the talent_slug from search_talent results.
| Name | Required | Description | Default |
|---|---|---|---|
| talent_slug | Yes | Talent slug from search_talent (e.g. 'john-doe'). Also accepts '@john-doe' or a himalayas.app/@slug profile URL. |
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
With no annotations provided, the description carries the full burden of behavioral disclosure. It communicates that this is a read operation that returns a set of fields (bio, experiences, education, tech stack, social links), which adds useful context about the response content. However, it does not mention failure behavior when the slug doesn't resolve, authentication requirements, or the shape of the returned object, leaving some gaps for a no-annotation 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?
Two sentences with zero waste. The purpose is front-loaded first, and the usage guidance second. Every clause adds value — no filler, no repetition of the schema, and no redundant information. This is an ideal length for a single-record fetch tool.
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 one-parameter fetch tool with no output schema and no annotations, this description is nearly complete. It covers what the tool returns, and the schema documents the parameter format. The only missing element is behavior on invalid or non-existent slugs, which is a minor edge case for a simple GET-style tool. This is 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 description coverage is 100%, so the schema already documents the talent_slug parameter including accepted formats ('john-doe', '@john-doe', or a himalayas.app URL). The description adds the provenance tip that the slug comes from search_talent results, which is a minor but useful addition. Since the schema does the heavy lifting, a baseline 3 is appropriate.
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 states a clear verb, 'Get', and resource, 'full details for a candidate', and enumerates specific content: bio, experiences, education, tech stack, social links. The 'and more' is slightly vague, but the structure clearly distinguishes this from the sibling search_talent, which finds candidates rather than fetching a single profile. The purpose is unambiguous for an agent.
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 gives explicit context on how to invoke the tool: 'Use the talent_slug from search_talent results.' This tells the agent where to obtain the required parameter, which is the primary usage question for a get-by-slug tool. It doesn't state exclusions or alternatives, but for a single-record fetch there are no natural sibling alternatives, so this level of guidance is appropriate.
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.