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

The description adds useful output behavior ('structured findings with confidence scores'), which is beyond the schema. However, annotations indicate readOnlyHint=false and idempotentHint=false, and the description does not clarify whether any side effects occur. It neither contradicts annotations nor fully explains behavioral 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 concise, front-loaded, and contains no filler. It communicates the core purpose and output in two short sentences, making it easy for an agent to parse quickly.

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?

The tool has three parameters, all schematically described, and no output schema. The description adequately covers purpose, analysis types, and high-level output. It could detail the return structure further, but for a straightforward analysis tool it 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 100%, so the baseline is 3. The description's mention of analysis types aligns with the 'analysis_type' enum, but it does not add meaning beyond what the schema already provides. The 'did' and 'data' parameters are also not further explained.

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 uses a specific verb ('Analyze') and resource ('data'), and clearly enumerates the analysis types: trends, anomalies, forecasts, or comparisons. It also mentions the return type (structured findings), distinguishing it from sibling tools like 'decide' and 'reason'.

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 clear context for when to use the tool by listing the supported analysis types (e.g., trends, anomalies, forecasts, comparisons). It does not explicitly mention alternatives or exclusions, but the use cases are sufficiently clear to guide selection.

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