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

Annotations already declare readOnly, openWorld, and idempotent. The description adds useful behavioral context: Jev-powered with automatic Groq-hosted LLM fallback and a sub-4.5s guarantee. It does not cover auth, rate limits, or error behavior, but adds meaningful beyond-annotation detail.

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 three tightly structured sentences, front-loading the core classification purpose and categories. Behavioral details follow, and there is no wasted text.

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 single-parameter classification tool with an output schema and rich annotations, the description is largely complete. It explains outputs, fallback behavior, and latency, but omits when to choose it over semantically similar siblings.

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 coverage is 100%, and the only parameter (text) is fully described in the schema as up to 4000 characters. The description adds no additional parameter semantics, so baseline 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 states a specific verb and resource: classify text into intent categories (question, request, complaint, compliment, other). It also mentions confidence and alternate label outputs. However, it does not explicitly differentiate from siblings like sentiment or classify, which could overlap.

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 usage through its purpose but gives no when-to-use guidance, no alternatives, and no exclusions. 'Try GET /intent/sample' is a sample endpoint hint, not usage guidance against siblings such as classify or sentiment.

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