list_companiesReturns top AI/ML companies by active role count, optionally with average salary. Useful for discovering who is hiring in AI.
| Name | Required | Description | Default |
|---|---|---|---|
| limit | No | Max companies (default 20, max 50) |
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
With no annotations, the description carries the burden of behavioral disclosure. It explains the primary behavior (top companies by active role count) but introduces ambiguity by saying 'optionally with average salary' without a corresponding parameter in the schema, suggesting an unsupported option. It lacks details on authentication, rate limits, sorting order, or response format.
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 concise: two sentences with no redundant wording. The first sentence states the core functionality, and the second adds a practical use case, making it easy to scan.
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 one optional parameter and no output schema, the description is adequate but has gaps. It doesn't clarify the output structure, whether results are sorted descending, or how the optional average salary is requested or represented. The ambiguity about salary reduces 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?
Schema description coverage for the sole parameter 'limit' is 100%, with explicit default and max values. The description adds no extra meaning for this parameter. However, the mention of 'average salary' as optional is misleading and may confuse the agent about whether additional parameters exist.
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 'Returns top AI/ML companies by active role count', using a specific verb and resource. It distinguishes from siblings like get_company (single company) and get_trending_companies (trending) by emphasizing hiring activity in AI.
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 phrase 'Useful for discovering who is hiring in AI' implies a usage scenario but does not explicitly compare with alternatives or state when not to use it. There is no mention of alternative tools like get_company or get_trending_companies, leaving the guidance implicit.
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.