list batches

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

With no annotations provided, the description carries the full burden and does so thoroughly: it discloses the bare JSON array response, the 100-object cap, the stored-vs-live status caveat, and the feature-flag failure mode. This goes well beyond a minimal definition.

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 core purpose and route, and the Returns/Notes structure makes the dense information scannable. The field enumeration and status list are long but necessary given the absence of an output schema, so it earns a high score rather than a perfect one.

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?

The description covers the full response shape, field semantics, status enum, nullable fields, pagination cap, failure mode, and the live-refresh caveat. Nothing essential is missing for an agent to select and invoke this tool correctly.

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 has zero parameters, so the baseline is 4; the description confirms no parameters are needed beyond the implicit workspace scope. There is no parameter ambiguity to resolve.

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 action and resource: listing the workspace's batch inference jobs, newest first, with status, request counts, and billed cost. This clearly distinguishes it from sibling tools like create_batch, get_batch, and cancel_batch.

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 identifies the scope (this workspace), the HTTP route, and the read-only API-key scope, and notes the feature-flag 404 that signals when the capability is unavailable. It also implicitly routes the agent to GET /v1/batches/{id} for live status refreshes, but it does not explicitly enumerate when to prefer get_batch versus list_batches.

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