get_companyGet detailed profile for an AI company: total open roles, salary range, top tags/skills, workplace distribution, and apply URL. Use after list_companies or search_jobs to learn more about a specific employer.
| Name | Required | Description | Default |
|---|---|---|---|
| slug | Yes | Company slug (e.g. 'anthropic', 'openai', 'deepmind'). Use the slug from search_jobs or list_companies results. |
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
With no annotations, the description carries the burden of disclosing behavior. It enumerates the output fields, which is helpful, but it does not mention error conditions, authentication, rate limits, or any side effects. For a read-only tool, the description is adequate but not richly transparent.
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 long, front-loads the tool's purpose and return contents, and immediately follows with usage guidance. Every word earns its place with zero redundancy.
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?
Given the tool is simple (one parameter, no output schema), the description covers the main return fields and provides usage context. It lacks mention of possible empty results or errors, but overall it is sufficiently complete for an AI agent to invoke 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% and the parameter 'slug' already includes an example and instruction to use the slug from search_jobs or list_companies results. The description reinforces this usage but adds no new semantic detail beyond the schema, so it meets the baseline without elevating.
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 gets a detailed company profile and enumerates specific contents (open roles, salary range, top tags/skills, workplace distribution, apply URL). This distinguishes it from sibling tools like get_job or get_salary_data, which focus on other entities.
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 advises using this tool after list_companies or search_jobs to learn more about an employer, providing clear context. However, it does not mention when not to use it or alternative tools, so it stops short of full exclusion guidance.
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.
Most tools have clear, distinct purposes (e.g., search_jobs vs get_job vs get_similar_jobs). The main ambiguity is between match_jobs and analyze_application_readiness, both of which assess candidate-job fit, though one ranks multiple jobs and the other evaluates readiness for a specific role. The application flow steps are well-separated.
All tool names consistently follow the snake_case verb_noun pattern (e.g., get_company, list_companies, apply_to_job). Even longer names like analyze_application_readiness and compile_job_specific_resume adhere to this convention, with no mixed casing or inconsistent verb styles.
With 20 tools, the server is on the heavier side for a job board, though the breadth of features (search, company info, salary, application, interview tracking, and product sales) partially justifies the count. Some redundancy exists (e.g., get_trending_companies vs list_companies, get_stats vs get_salary_data), making the set feel slightly bloated.
The toolset covers core job search and application workflows, but there is no way to list or track submitted applications, view application status, or withdraw an application. Post-application features are limited to interview outcomes, leaving obvious lifecycle gaps for a candidate-facing job platform.