get_company_details
Get full details for a company including about, tech stack, benefits, open positions, and social links
Input Schema
| Name | Required | Description | Default |
|---|---|---|---|
| company_slug | Yes | Company slug (e.g., 'stripe', 'gitlab') |
Get full details for a company including about, tech stack, benefits, open positions, and social links
| Name | Required | Description | Default |
|---|---|---|---|
| company_slug | Yes | Company slug (e.g., 'stripe', 'gitlab') |
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
No annotations are present, so the description carries the full burden of behavioral disclosure. It only states that the tool retrieves details, implying a read-only operation, but does not disclose additional behaviors such as authentication requirements, response format, pagination, or potential latency. This is a significant gap for a tool that might return a large payload.
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 a single, efficient sentence that front-loads the core action ('Get full details') and then lists the key content areas. There is no redundant text or unnecessary embellishment, making it highly concise and well-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 adequately conveys the scope of the data returned, but it omits any indication of the output schema or error conditions. Given that it's a simple tool with one parameter, this might be sufficient, yet the lack of detail on response structure (e.g., whether open positions are a list of IDs or full objects) leaves some ambiguity. It is complete enough for basic use but not fully comprehensive.
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 only parameter, company_slug, is already described in the schema with a concrete example. The tool description adds no further semantic detail beyond what the schema provides. Since schema description coverage is 100%, the baseline score of 3 applies; the description does not 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?
The description clearly states the tool retrieves full company details and enumerates specific content areas (about, tech stack, benefits, open positions, social links). However, it does not explicitly differentiate itself from sibling tools like get_company_profile or get_company_perks, which could lead to ambiguity for an agent deciding between them.
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?
No guidance is provided on when to use this tool versus alternatives. There is no mention of when to prefer get_company_details over get_company_profile or other related tools, nor any exclusions for cases where a lighter-weight call would suffice. The agent is left to infer usage from the 'full details' phrasing.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
Add one secure layer between your agents and this server.
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.