ml_process_optimization

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

Annotations (readOnlyHint, idempotentHint, openWorldHint) already disclose key behavioral traits. The description adds that it analyzes 'task durations and reassignment patterns,' which provides context beyond annotations. No contradictions, but it does not reveal potential side effects or limitations like rate limits or data scope.

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 12-word sentence that immediately conveys the core action. Every word is meaningful, no redundancy. It is optimally concise for a simple tool.

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 read-only analysis tool with 2 parameters and no output schema, the description is mostly adequate. It explains what it does and the analysis method. However, it does not describe the output format (e.g., list of bottlenecks, metrics, severity), which would help an agent interpret results. Considering the tool's simplicity, a moderate score is fair.

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 covers 100% of parameters with clear descriptions (table: process table examples, days: default 90). The description adds no further parameter details. Baseline 3 is appropriate as the schema does 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: 'Identify process bottlenecks using analysis of task durations and reassignment patterns.' The verb 'identify' and resource 'process bottlenecks' are specific. While it distinguishes from siblings like ml_detect_anomalies or ml_forecast_incidents, it does not explicitly differentiate from all ML tools, leaving slight ambiguity.

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 does not provide any guidance on when to use this tool versus alternatives (e.g., ml_detect_anomalies or ml_forecast_incidents). It lacks explicit when-to-use or when-not-to-use instructions, leaving the agent to infer 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.