get_document_suggestions

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

Beyond the readOnlyHint annotation, the description discloses that results are limited to IDs of existing objects and clarifies the classifier-based nature of the suggestions. This adds useful behavioral context, though it does not elaborate on possible empty results or response shape.

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?

Two sentences, no filler, with the primary purpose front-loaded and the alternative guidance placed second. Every sentence contributes either scope or routing information.

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, read-only tool with no output schema, the description covers the suggestion fields, the existing-only return constraint, and the sibling distinction. It does not specify the exact response format, but the provided detail is sufficient for correct selection and invocation.

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 already documents the sole parameter 'id' with 100% coverage ('The document ID to get suggestions for'), so the description adds no new parameter-level semantics. The baseline score of 3 applies because the schema handles the documentation burden.

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 ('Get classifier-based suggestions') and resource (a document's correspondent, tags, document type, storage path, and dates), which is unambiguous. It also distinguishes itself from get_document_ai_suggestions by noting the classifier approach and the constraint that it returns IDs of EXISTING objects only.

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 explicitly names the alternative get_document_ai_suggestions and the condition for choosing it: LLM suggestions that may propose a title and new objects. This provides a clear when-to-use/when-not-to-use rule, so an agent can route between the two suggestion tools without further exploration.

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