ml_process_optimization

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

Annotations already declare readOnlyHint and openWorldHint, so the safety profile is clear. The description adds useful methodology context (task durations and reassignment patterns) but does not disclose output format, data requirements, or any caveats. It is consistent with the read-only annotation.

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, well-structured sentence that front-loads the action and efficiently conveys the tool's purpose without redundancy.

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 two parameters, both documented, and annotations cover safety. However, with no output schema, the description omits what the result looks like (e.g., a list of bottlenecks, metrics), and it does not clarify what constitutes a bottleneck or how the output is presented. This leaves some gaps for an ML analysis tool.

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?

Both parameters are fully described in the schema (days with default 90, table with examples). The description does not add any additional meaning beyond the schema, so a baseline score of 3 is appropriate.

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 ('identify') and resource ('process bottlenecks') and explains the method ('analysis of task durations and reassignment patterns'). It clearly differentiates from sibling ML tools by targeting process bottlenecks, though it does not explicitly name alternatives.

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 when to use the tool (when process bottlenecks need identification) but provides no explicit guidance on when not to use it or which alternative tools to consider. It neither mentions prerequisites nor exclusions.

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