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

With no annotations provided, the description carries the full burden. It discloses the salary extraction side-effect ('If salary not provided, extract it from the fetched job data') which is helpful behavioral context, and the return type. However, it doesn't disclose side effects like whether the job is removed from the temp list, idempotency, or error behaviors.

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 with clear sections (Args, Returns). It adds a second sentence about salary extraction that is valuable. Minor redundancy: the Returns line restates the obvious outcome of a save action. Overall well-organized and front-loaded.

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?

There is an output schema that presumably documents the return value, reducing burden there. The tool has only 2 params, making it relatively simple. The description covers the core behavior and the notable optional salary-extraction side effect. It doesn't address failure cases or edge conditions, but for a simple save operation with an output schema, this is reasonably 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. It does reasonably: job_id is explained as 'The ID of the job to save' and salary as 'The salary of the job to save', plus the extraction fallback behavior. A stronger definition of what 'extract' means exactly and expected salary format would push this higher, but the semantics are adequately clarified given zero schema documentation.

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 verb (save), the resource (job), and the action context (from temporary list into candidate's saved folder). It distinguishes the purpose reasonably from its sibling search_jobs, though it doesn't explicitly name the sibling as an alternative. The 'Args' and 'Returns' sections add useful structure.

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 implies usage context (saving from a temporary list into a saved folder) but does not explicitly state when to use this vs search_jobs. It mentions an optional behavior (extract salary if not provided) but gives no exclusions, prerequisites, or explicit alternative guidance.

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

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

No annotations are provided, so the description carries full burden for behavioral disclosure. It reveals that jobs are 'stored temporarily,' which adds transparency about side effects, but it doesn't disclose data freshness, pagination behavior, API rate limits, or whether this is a read-only vs write operation. For a tool that fetches external data and stores it, more behavioral context would be expected.

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 readable, with a brief summary line followed by a structured Args/Returns section. It's efficient without wasted words. The format mirrors a Python docstring which is familiar to many agents, aiding comprehension.

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?

With zero annotations, no sibling differentiation, and a 0% schema coverage, this description leaves significant gaps. It doesn't mention how results are ordered, what constitutes 'key info' in the returned dictionaries, whether location requires a specific format or country, or how temporary storage interacts with the save_job sibling. An output schema exists, which helps explain return values, but the description still lacks operational context for a tool with 3 parameters and external API side effects.

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 for parameter documentation. It lists role, location, and max_results with terse one-line descriptions that essentially restate the schema field names ('The role to search for', 'The location to search for', 'The maximum number of jobs to return'). 'Role' and 'location' are ambiguous - role of what (job title? seniority?), and no format or examples are provided for any parameter.

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 states the tool 'Fetch(es) jobs using JSearch API and store(s) them temporarily. Return(s) key info.' This provides a specific verb (fetch) and a resource (jobs via JSearch API), distinguishing it from the sibling 'save_job' tool which implies persistence. However, it doesn't explicitly contrast against save_job in the description, though the 'store them temporarily' phrasing hints at the difference.

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?

There is no explicit guidance on when to use this tool versus save_job or other alternatives. The description implies 'fetching jobs' is the use case, but lacks any when/when-not statements or mention of the sibling save_job tool. The distinction between temporary storage here and the save_job sibling is only implicit.

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