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

The description adds valuable behavioral details beyond the annotations: if any row has the wrong column count, the whole write is rejected and nothing is written, and appending to an empty dataset infers column count from the first row. This atomic failure behavior is not expressed in the annotations or schema.

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 composed of three purposeful sentences: the first states the action, the second explains the key behavioral guarantee, and the third handles an edge case. Every sentence adds necessary information with 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 three-parameter mutation tool with no output schema, the description covers the core call mechanics, validation rules, failure behavior, and an important edge case. An agent has enough context to invoke the tool correctly without ambiguity.

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 description coverage is only 33%, but the description compensates for the most important parameter, rows, by specifying that rows are data-only, must match the existing column count, and are rejected otherwise. projectId and datasetId are not described in detail, but their names plus the 'existing dataset' context make their meaning obvious.

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 and resource combination—'Add rows to the end of an existing dataset'—which clearly distinguishes it from create, update, and delete operations. The phrase 'existing dataset' also prevents confusion with dataset creation 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 clear context for when appending is appropriate: only new rows are sent, existing rows are untouched, and there is no need to read the table first. It does not explicitly name an alternative tool like update_dataset, but the usage context is clear enough for an agent to choose it correctly.

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