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

Annotations cover the safety profile (readOnly, idempotent, non-destructive), and the description adds genuinely new behavioral context: the 200-item batch cap, the blank-first/changed-second ordering, and the 'done' termination signal. It stops short of describing the shape of the returned prompt objects, which the missing output schema leaves to inference.

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?

Dense and front-loaded with the core purpose first, then workflow, then redo, then auth. The sentences are packed and slightly run-on, but every clause carries information the agent needs.

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 no output schema and no annotations explaining return semantics, the description supplies the essential pieces an agent needs: batch size, ordering, the 'done' sentinel, and the round-trip pairing with fill_ai_cells. It leaves the concrete structure of each prompt entry (fields other than row/basis) unspecified.

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 only 25% schema coverage, the description carries the burden well: it explains redo ('also re-answers generated values that came from outside the instruction'), the 200/limit batching, that slug identifies the table, and that column is optional by implication. It does not explicitly clarify the column-scoping parameter, keeping it just short of full compensation.

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 the specific resource ('the next prompts to answer for a table's AI columns') and its batching behavior, making the read-batch nature unmistakable. Its pairing with fill_ai_cells effectively distinguishes it from the write sibling, though the leading phrase is a noun clause rather than a crisp verb+resource statement.

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 spells out the full workflow: fetch prompts, answer each yourself, send back via fill_ai_cells with row and basis, repeat until 'done'. The alternative (fill_ai_cells) is named with the exact condition that selects it, and the token requirement is flagged.

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