Get a pattern evidence plan

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

Annotations already establish read-only, idempotent, and non-destructive behavior. The description adds value by specifying the exact evidence-plan payload returned — hypothesis, measurements, experiment, rejection criteria, and deletion triggers — which is especially useful since there is no output schema. It does not discuss behavior for missing patterns or invalid cases, but the safety profile is well covered by annotations.

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 two short sentences with no filler. It front-loads the concrete returned fields and ends with a practical use trigger. Every sentence earns its place.

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 description usefully lists the returned evidence-plan elements, partially compensating for the missing output schema. However, given the highly nested case parameter and zero parameter semantics in the description, an agent may struggle to construct a valid request or understand invalid-input behavior.

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 0%, so the description must compensate, but it only says 'one pattern in one case.' It gives almost no guidance on how to populate the large nested case object or what values the pattern string should take. The rich schema structure for case is not reflected in the description.

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 opens with a specific verb and resource: 'Return the hypothesis, measurements, experiment, rejection criteria, and deletion triggers for one pattern in one case.' This clearly scopes the tool to a single pattern and case, which distinguishes it from broader sibling tools like query_pattern_graph and plan_pattern_adoption.

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 second sentence gives an explicit use condition: 'Use when a recommendation needs proof before implementation.' However, it does not name alternative tools or state when not to use this tool, so exclusion guidance is left implicit.

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