Index Dataset for RAG

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

Beyond the idempotentHint annotation, the description reveals async behavior, internal steps (splitting, embedding, storing), typical completion time (10–60 seconds), and the effect of re-indexing with fresh embeddings. This adds substantial context not captured in 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 front-loaded with a one-sentence summary, followed by concise behavioral details and a clear Args section. Every sentence adds value, and the structure is easy to scan.

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 one parameter, an output schema, and a clear description of the operation, the tool is fully specified. It covers prerequisites, async nature, idempotency, and the relationship to sibling tools.

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 schema only specifies an integer dataset_id, but the description adds the source and meaning: 'ID of the built dataset to index (from job.status after dataset.build).' This provides exactly the necessary context for the parameter.

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 explicitly states the verb 'Trigger semantic indexing' and the resource 'dataset', distinguishing it from siblings like dataset.build and dataset.chunks. It also notes it is required before using dataset.chunks, which clarifies its role.

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 clearly states when to use: 'required before using dataset.chunks (Pro+ plan)' and the post-condition 'After indexing, use dataset.chunks(dataset_id).' It also references the prerequisite from job.status after dataset.build, giving a clear workflow.

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