get_document_ai_suggestions

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

Beyond the readOnlyHint annotation, the description discloses that the tool requires AI to be enabled and fails with a specific error otherwise. It also reveals the shape of its output (IDs for existing objects, suggested names for new ones), which is useful behavioral context.

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 dense but not bloated: the first sentence gives the core action and result contents, the second adds the prerequisite and failure behavior, and the third routes to the correct alternative. 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?

With only one simple parameter and no output schema, the description provides enough context: what suggestions are returned, what prerequisite must hold, what happens if it doesn't, and which sibling to use instead. Nothing essential is missing.

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 single id parameter is already documented as 'The document ID', so the description adds little parametric meaning. It refers to 'a document' in passing but does not need to elaborate given the schema covers it.

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 ('Get LLM-based suggestions for a document') and enumerates exactly what is returned. It also distinguishes this tool from its closest sibling, get_document_suggestions, by naming the AI/non-AI difference.

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?

It states the AI-enabled prerequisite and the exact failure mode if unmet, and explicitly instructs to use get_document_suggestions for classifier suggestions that work without AI. The when-to-use and when-not-to-use guidance is unambiguous.

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