Ingero

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

With no annotations provided, the description carries the full burden of behavioral disclosure. It effectively adds context beyond the input schema by explaining the return behavior: statistics vary based on database size (p50/p95/p99 for small DBs, count/avg/min/max for large DBs). This clarifies what the tool does operationally, though it doesn't cover aspects like rate limits, error handling, or performance implications.

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 appropriately sized and front-loaded, with two concise sentences that directly convey the tool's function and output behavior. Every sentence earns its place by providing essential information without redundancy or fluff, making it efficient for an AI agent to parse.

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 moderate complexity (2 parameters, no annotations, but with an output schema), the description is reasonably complete. It explains the core functionality and output variations, and since an output schema exists, it doesn't need to detail return values. However, it lacks context on error cases or integration with sibling tools, leaving minor gaps.

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 100% description coverage, so the schema already documents both parameters ('since' for time range and 'tsc' for telegraphic compression). The description adds no additional meaning about parameters beyond what the schema provides, such as usage examples or constraints. Thus, it meets the baseline score of 3, as the schema handles the heavy lifting.

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 CUDA and host operation statistics.' It specifies the verb ('Get') and resource ('CUDA and host operation statistics'), making the function unambiguous. However, it doesn't explicitly differentiate this from sibling tools like 'get_causal_chains' or 'get_stacks', which might also retrieve trace-related data, so it doesn't reach the highest 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 mentions differences in output based on database size (small vs. large DBs), but this is about behavioral output rather than usage context. There's no indication of prerequisites, when-not-to-use scenarios, or comparisons to sibling tools, leaving the agent without clear selection criteria.

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