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

With no annotations at all, the description carries the full burden and does real work: it discloses data provenance (scam-filtered), freshness (updated hourly), matching semantics (relevance over title + company + description), and the shape of returned records including apply_url vs detail_url. Gaps remain on pagination beyond the 25-item cap, rate limits, and what an empty result looks like.

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?

Three tight sentences, front-loaded with scale and freshness, then query behavior, then return shape. The hard-coded '140,528+' count is marketing detail that will drift out of date, but nothing else is wasted.

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 an 11-parameter search tool with no annotations and no output schema, the description covers the essentials: what the corpus is, how queries match, and what fields come back — effectively substituting for a missing output schema. It stops short of describing result paging, defaults for sort, or behavior when filters conflict.

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 already 82%, so the baseline is 3, but the description adds genuine meaning beyond the schema by explaining how the free-text query is matched across title, company, and description, and by noting salary is only present 'when disclosed' — which matters for the has_salary filter and sort=salary.

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?

States a specific verb and resource ('Search ... remote jobs') plus the corpus size and freshness, so the agent knows exactly what it retrieves. It does not differentiate itself from siblings such as get_job or job_facets, so an agent must infer that this is the discovery step rather than the detail or facet step.

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?

Usage is only implied: 'Natural-language queries OK' signals free-text discovery, but there is no explicit when-to-use vs get_job/job_facets, no mention that this returns listings rather than counts or full detail, and no exclusions or prerequisites. An agent can guess the role but is not routed.

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