jobs_search

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

With no annotations, the description carries the burden well: it discloses the write side effect ("saves what it finds"), the external dependency ("via Apify, if configured"), the ranking order ("best first"), and where partial failure surfaces ("Check `source_error` if LinkedIn failed"). Gaps remain around rate limits, whether saving is idempotent/deduplicated, and what happens when Apify is not configured.

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?

Front-loaded with the action and the pipeline order, and the single inline example earns its place by pinning down argument phrasing. Slightly dense with parenthetical asides, but there is no filler sentence.

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 four-parameter discovery tool with no output schema and no annotations, the description covers the essential contract: what it triggers, what it persists, how results are ordered, and the failure signal to inspect. Missing detail on paging/limit behavior and duplicate handling keeps it from being fully 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?

Schema description coverage is 0%, so the description must compensate, and it only partly does: `query` and `location` get a worked example, which fixes the expected string format and clarifies that location defaults sensibly. `limit` and `use_linkedin` are never mentioned despite being the two arguments that control cost and external calls, so half the surface remains undocumented.

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 concrete verb+resource ("Search internships") and unpacks the full operation: run a LinkedIn search for query/location, persist results, then return matches from the whole saved pool including company boards, ranked best-first. That is far more specific than the name alone and implicitly separates it from jobs_list/job_get. It never names a sibling tool, so it stops short of the top mark.

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?

"Search internships now" signals this is the live-search-and-save path, and the Apify conditional hints at the prerequisite for the LinkedIn leg. But there is no explicit when-not guidance and no reference to jobs_list, jobs_fetch, or job_add, so an agent must infer that this tool is for discovering new postings rather than reading the existing store.

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