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

The description substantially exceeds the annotations by disclosing that indexing is asynchronous, splits data into RAG-ready chunks, generates embeddings, stores them, is idempotent but re-indexes with fresh embeddings, and typically completes in 10–60 seconds. It also notes the Pro+ plan requirement and the correct retrieval method afterward. No contradiction with annotations exists.

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 primary action and requirement, then provides concise behavioral details, timing expectations, and parameter guidance. Every sentence adds value, and the Args section cleanly closes the description without redundancy.

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 async indexing tool, the description covers the full workflow: prerequisite (dataset.build), downstream use (dataset.chunks), behavior (chunking, embeddings, storage), idempotency, timing, and plan requirement. An output schema exists, so not detailing return values is acceptable. The context is complete for safe and correct 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?

With 0% schema description coverage, the description fully compensates by explaining dataset_id as 'ID of the built dataset to index' and gives provenance: 'from job.status after dataset.build'. This tells the agent where to obtain the value, which is more useful than the schema's bare integer type and title.

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: 'Trigger semantic indexing for a dataset'. It clearly distinguishes this from siblings by stating it is 'required before using dataset.chunks' and that indexing prepares a dataset for semantic search. The scope—indexing a built dataset for RAG—is unambiguous.

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 clear workflow context: index 'required before using dataset.chunks' and the dataset_id comes 'from job.status after dataset.build'. It does not explicitly state when not to use it or compare it with alternatives like dataset.search or dataset.export, but the prerequisite and downstream steps are clearly defined.

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