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

With no annotations provided, the description carries full responsibility for behavioral disclosure. It explicitly states that the tool is read-only, does not trigger new analysis, and does not consume AI credits. It also explains the behavior when analysis is absent (hasAnalysis false, message field) and that empty fields are omitted. This is thorough and transparent.

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 front-loaded with the core purpose and then provides essential behavioral context. The long list of possible return fields is justified because there is no output schema, so the agent needs that information to understand potential responses. It is somewhat long, but every section 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?

Given the absence of an output schema, the description is exceptionally complete. It covers purpose, side effects, the uninitialized state and fallback action, and enumerates the full set of possible returned fields. It leaves no critical gaps for an agent to invoke the tool correctly and interpret the response.

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 fully describes the single `id` parameter, including the ObjectId/case number fallback logic. The description adds no parameter-specific meaning, but since schema coverage is 100%, the baseline of 3 is appropriate. The description's focus on return fields is useful but not directly about parameter semantics.

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 object: 'Reads any existing AI analysis for a case.' It clearly distinguishes this from sibling tools by emphasizing 'existing' and explicitly contrasting with analyze_case. The read-only nature is also stated upfront.

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 gives explicit guidance on when to use this tool: to retrieve an already-generated analysis. It also specifies when not to use it: if hasAnalysis is false, the agent should call analyze_case instead. This direct alternative naming makes the usage boundaries highly clear.

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