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

With no annotations, the description carries full burden. It adds useful context (faithful port of a reproducible Python model, estimates not commitments) but does not discuss error handling, idempotency, or authorization needs. The description adds value beyond the schema but is not exhaustive.

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 only three sentences, front-loads the main action, and contains no redundant information. Every sentence serves a purpose.

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 tool with no output schema and 5 optional parameters, the description adequately explains the tool's purpose and limitations. However, it could mention the output format (e.g., a single number or percentage) to improve completeness.

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 for all 5 parameters, so the baseline is 3. The description does not add additional parameter-specific meaning beyond what the schema provides, such as how parameters interact with the model.

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 action ('Estimate the ROI') and the specific resource ('adding a Mingxin FX100 KV-cache storage tier to a GPU inference cluster'). It differentiates from sibling tools like 'query_benchmark' and 'search_mingxin_docs' by focusing on estimation of ROI.

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 context that the results are 'mid-scenario estimates, not commitments', setting expectations. However, it does not explicitly state when to use this tool versus alternatives or when not to use it, which would improve clarity.

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