list_jobs

Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?

With no annotations provided, the description carries the full behavioral disclosure burden. It does well by explicitly describing case-insensitive matching, comma-separated keyword handling, the 'any keyword' match semantics, the blank-keyword fallback to set_department, and the 'all' escape hatch. It also mentions the returned count. Minor omissions like error behavior or pagination are acceptable given the tool's simplicity.

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 compact and well-structured: two introductory sentences establish the core action and return value, followed by a concise argument list. Every sentence adds meaningful information, and the most important information about what the tool does is front-loaded. No filler or redundant restatement of the tool name.

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 tool with no annotations and no output schema, the description provides solid coverage of its inputs, behavior, and a key return value (total count). It lacks an explicit description of the posting object's shape, but the phrase 'list open job postings' implies a collection of job records, and the sibling set provides enough context for an agent to use the tool correctly. Overall, the description is nearly complete for a simple list operation.

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 input schema provides only types and a default, with 0% schema description coverage. The description fully compensates by defining job_board_name as the company's slug from jobs.ashbyhq.com/{slug} and by thoroughly explaining keyword semantics, including filtering target, case-insensitivity, multiple keywords, and special reserved values. This gives an agent everything needed to fill both parameters correctly.

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 opens with a specific verb and resource: 'List open job postings for a company's public Ashby job board.' It also clearly states it returns the total number of open postings, and the keyword filter behavior distinguishes it from sibling tools like get_job or get_latest_jobs. This is unambiguous and not a tautology.

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 explains when to use the tool: for any public Ashby job board, optionally filtered by keyword. It also gives concrete usage guidance for the keyword parameter, including the special values 'blank' and 'all', and references set_department as a way to persist a department filter. It does not explicitly name alternative tools or say when not to use it, but the context strongly implies the intended use.

Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.