Scrape job listings from company ATS boards: Greenhouse, Lever, Workday, and Ashby. No auth required — uses their public job APIs.
| Name | Required | Description | Default |
|---|---|---|---|
| company | Yes | Company slug as used in their ATS URL (e.g. "stripe" for boards.greenhouse.io/stripe) | |
| platform | No | ATS platform (default: auto-detect) | |
| maxResults | No | Max jobs (default 50) |
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
With no annotations provided, the description covers basic behavior (no auth, public APIs). It does not disclose potential rate limits, error handling, or whether all companies are supported, leaving some gaps.
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-loaded with the core purpose, and contains no unnecessary words. It is efficient and easy to parse.
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 omits details about return format or limitations, which would be helpful given no output schema. While functional, it could be more complete.
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 schema coverage is 100%, and the description adds value by providing examples for the 'company' parameter and listing supported platforms for 'platform'. This goes beyond the schema descriptions.
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 scrapes job listings from specific ATS boards (Greenhouse, Lever, Workday, Ashby), providing a specific verb and resource. It distinguishes itself from sibling tools that target different sources (e.g., search_linkedin_jobs).
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 notes 'No auth required' and specifies it uses public job APIs, giving clear context for when to use this tool. However, it does not explicitly compare to alternatives or state when not to use it.
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 target unique data sources or specific actions (e.g., search_zillow vs. get_zillow_property_details are clearly sequential). A few LinkedIn-related tools (find_linkedin_candidates vs. search_linkedin_employees) have overlapping purposes but their descriptions clarify distinct use cases. Overall, confusion is minimal and descriptions resolve ambiguity.
All tools follow a consistent verb_noun pattern in snake_case, using verbs like search, get, find, scrape, analyze, lookup, resolve, and verify. The pattern is predictable across the entire set, making it easy for an agent to infer function from name.
With 32 tools, the server exceeds the 'too many' threshold of 25+. While the broad scope of web data mining justifies some diversity, the count is unwieldy and could overwhelm an agent's selection process. A smaller, more focused set per domain would improve coherence.
The toolset covers a wide range of data retrieval needs: company research, real estate, job listings, academic research, and government records. For a read-only data aggregation service, there are no major lifecycle gaps, though some subdomains like social media scraping only cover Reddit and LinkedIn, missing other platforms. Overall, it is reasonably complete for its stated purpose.
