Add rows

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

Annotations already declare this is a non-read-only, non-destructive, non-idempotent write, so the description is not carrying the safety burden alone. It still adds real behavioral context: duplicate handling on the index column with a `skipped_duplicates` result, that inserted rows are returned, the 100-row cap, and that the call is free. Missing only permission/auth expectations.

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?

Very short and front-loaded: the limit and row shape come first, then duplicate behavior, return value, and next step. The telegraphic fragments ('Then `enrich` with `dataset_id`', bare 'free') are efficient but slightly under-explained, keeping it from a 5.

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 two-parameter mutation tool with no output schema, it covers the key facts an agent needs: cap, row format, duplicate semantics, what is returned, cost, and the natural next call. Auth requirements and failure modes are the notable omissions.

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?

Schema coverage is 100%, so the baseline would be 3. The description goes beyond it by introducing the index-column duplicate concept, which is not expressed in the schema and explains how row contents are interpreted, plus restating the 100-item cap in operational terms.

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?

States a specific verb and resource ('Append up to 100 rows') with the row shape defined as objects keyed by input column name. It differentiates from the sibling `import_dataset_rows` for larger volumes, though the first sentence does not explicitly tie the operation to a dataset (that only emerges from the `dataset_id`/`id` reference).

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?

Gives an explicit alternative for the over-100 case ('More rows: `import_dataset_rows`') and sketches the follow-on workflow ('Then `enrich` with `dataset_id`'). It does not say when to prefer this over `update_dataset_row` for modifying existing rows, so routing against that sibling is left to inference.

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