get_trending_companiesCompanies posting the most AI/ML jobs in the last N days. Complements get_stats trending_tags_7d — while that answers 'what skills are trending', this answers 'who's hiring most aggressively'. Returns company name/slug/logo plus the count of new jobs in the window.
| Name | Required | Description | Default |
|---|---|---|---|
| days | No | Look-back window in days (default 7, max 30) | |
| limit | No | Max results (default 10, max 25) |
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
No annotations are provided, so the description carries the full burden. It discloses that the tool returns company name/slug/logo plus the count of new jobs, and that it counts jobs within the specified look-back window. This provides useful behavioral context beyond the name, though it doesn't explicitly state it's read-only (implied by 'get') or mention any side effects.
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 three sentences, front-loaded with the main purpose, then differentiation, then return value details. Every sentence contributes meaningful information with no fluff or repetition.
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 tool has only two optional parameters, no output schema, and no annotations. The description mentions the key output fields (company name/slug/logo and job count), the selection criterion (most AI/ML jobs), and the time window concept. It is sufficiently complete for an agent to understand what the tool does and what it returns.
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 baseline is 3. The description adds some context by explaining that 'days' controls the look-back window for job counts, but it doesn't add meaning for 'limit' beyond what the schema already specifies. Minimal additional semantic value over the schema.
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's purpose: to list companies posting the most AI/ML jobs in a given time window. It uses a specific verb+resource structure and explicitly contrasts with the sibling tool get_stats trending_tags_7d, distinguishing what this tool answers versus that one.
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 explicit when-to-use guidance by comparing to get_stats trending_tags_7d: 'while that answers what skills are trending, this answers who's hiring most aggressively'. This gives clear context for choosing this tool over the alternative.
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.