get_categories

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

No annotations are provided, so the description carries the full burden of behavioral disclosure. It states the tool retrieves aggregated job data but doesn't cover critical aspects like whether it's read-only (implied by 'Get'), rate limits, authentication needs, data freshness, or error handling. For a tool with zero annotation coverage, this leaves significant gaps in understanding its operational behavior.

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 a single, efficient sentence that front-loads the core functionality ('Get job counts by category/sector') and adds necessary detail ('with experience level breakdown (entry, mid, senior)'). There is no wasted verbiage, making it highly concise and well-structured for quick 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?

Given the tool's complexity (aggregating job data with breakdowns), no annotations, and no output schema, the description is minimally adequate. It specifies the data retrieved but lacks details on format, pagination, or error cases. For a read operation with no parameters, it meets basic needs but doesn't fully address potential agent uncertainties about behavior or output structure.

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 has 0 parameters with 100% coverage, so no parameter documentation is needed. The description adds value by explaining what data is retrieved ('job counts by category/sector with experience level breakdown'), which clarifies the output semantics beyond the empty schema. This compensates appropriately for the lack of parameters.

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's purpose: 'Get job counts by category/sector with experience level breakdown (entry, mid, senior).' It specifies the verb ('Get'), resource ('job counts'), and granularity ('by category/sector' with 'experience level breakdown'). However, it doesn't explicitly differentiate from siblings like 'get_stats_overview' or 'get_experience_levels', which might overlap in scope, preventing a perfect score.

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 provides no guidance on when to use this tool versus alternatives. It doesn't mention prerequisites, exclusions, or compare it to siblings such as 'get_stats_overview' or 'get_experience_levels', which could offer similar or complementary data. Without this context, the agent must infer usage from the purpose alone.

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