get_dataset

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

With no annotations provided, the description takes full burden. It discloses that the tool returns status, row count, timestamps, and indicates polling behavior. No contradictions, but it could mention if it's read-only or requires authentication, though not critical for a get operation.

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, front-loaded with purpose, followed by a use case. Every sentence adds value without redundancy or fluff.

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 0-param tool with no output schema, the description explains return fields and purpose. Slightly unclear how the dataset is identified (maybe from context), but overall complete for its simplicity.

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?

Zero parameters in schema, and schema coverage is 100%. The description doesn't need to add param info, but it explains the returned fields well, providing context beyond the empty schema.

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 uses a specific verb 'get' and resource 'single dataset's details' with explicit fields (dimension, status, row count, timestamps). It clearly differentiates from sibling tools like create_dataset, delete_dataset, list_datasets, and query_vectors.

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?

States a clear use case: 'polling indexing progress after ingest.' This implies when to use (check status) and hints at not using for other purposes like creation or listing, though explicit exclusions are absent.

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