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

Annotations already cover read-only and idempotent behavior. The description adds meaningful behavioral context beyond that: rows are 'accepted AI craft' only, newest first, paginated via cursor, with references to workflow, node, lead, and aiUsage cost row. This gives the agent a solid picture of what a call returns and how it behaves.

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 dense but every clause earns its place: resource, row granularity, included fields, ordering, pagination, and intended use cases. It is front-loaded with the action and resource, and there is no filler.

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 read-only export tool with no required parameters and no output schema, the description is nearly complete. It explains what rows are returned and their content, plus pagination and ordering. It could mention the response envelope or cursor mechanics more explicitly, but it is sufficient for an agent to invoke it 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?

Input schema coverage is 100%, so the schema already documents every parameter. The description adds general pagination and ordering context but does not add per-parameter semantics beyond what the schema provides. Baseline 3 is appropriate.

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 names a specific verb and resource: 'Export craft audit log rows for a workspace'. It goes further by itemizing the exact row contents (prompts, raw output, final text, attempt count, refs) and the row types (messages, notes, comments, replies, critic passes), making it easily distinguishable from the many sibling tools.

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 explicit use cases: 'diagnose a bad draft from what the model was actually shown, or to assemble a grading set'. It does not name alternatives or exclusion criteria, so it stops short of a full when-not-to-use guidance, but the context is clear enough for an agent to select this tool appropriately.

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