Update your company's tech stack on Himalayas. Pass technology names (e.g., 'React', 'Python') and they'll be matched to the Himalayas stack database. Requires employer authentication.
| Name | Required | Description | Default |
|---|---|---|---|
| stacks | Yes | Array of technology names (e.g., ['React', 'TypeScript', 'Python', 'AWS']) |
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
No annotations are provided, so the description carries full responsibility for behavioral disclosure. It mentions authentication and matching to the database, but does not disclose whether the update replaces the entire stack or appends to it, whether it is idempotent, or how errors are handled. For a write operation, these are significant missing details, so it falls short of comprehensive transparency.
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?
The description is two sentences, with the primary action front-loaded and the additional behavior (matching to database) placed second. There is no redundant information, and the wording is efficient. It earns a top score for conciseness and structure.
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 tool with a single parameter and no output schema, the description covers essential aspects: the action, the input format, the matching behavior, and the authentication prerequisite. It is sufficient for an agent to call it correctly, though it does not specify the return value or error scenarios. Since this is straightforward, a 4 reflects near-completeness.
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 input schema already documents the 'stacks' parameter with 100% coverage. The description adds value by explaining that technology names will be matched to the Himalayas stack database, which is not mention in the schema. This gives the agent insight into how the parameter is processed, making it more than a mere restatement.
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 ('Update'), the resource ('your company's tech stack'), and the platform ('on Himalayas'). It also differentiates from siblings like 'update_tech_stack' (likely personal) and 'update_company_profile' (general) by being specific to the company's tech stack. The mention of matching to the Himalayas stack database further clarifies its function.
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 provides clear context for when to use it: when updating a company's tech stack on Himalayas. It instructs users to pass technology names, implying the input format. However, it does not explicitly state when not to use it or name alternative tools (e.g., 'update_tech_stack' for personal profiles). The context is clear but excludes no alternatives, so a 4 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.