add_column

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

With no annotations provided, the description carries the full burden of behavioral disclosure. It explains the important side effect that existing rows are backfilled with the column's default value or null, and it documents both success and failure return shapes, including specific failure reasons such as duplicate columns and invalid enum definitions. It omits permission or reversibility details but covers the core mutation behavior well.

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 well-organized: purpose, behavioral consequence, usage trigger, arguments, return values, and example. Every section earns its place, and the key facts are front-loaded rather than buried. There is no redundant or filler content.

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 annotations and an opaque column object schema, the description covers backfill behavior, success and error returns, failure triggers, and a runnable example. It is not fully self-contained because the complete column definition shape lives in create_dataset, but it gives enough pointers for an agent to proceed.

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 0% description coverage, so the description must compensate. It meaningfully describes dataset as needing the exact name, and it explains that column uses the same shape as create_dataset, backed by an example. However, the full column definition is delegated to another tool rather than documented here, so agents still need cross-tool context to construct valid column objects.

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, 'Add a new typed column to an existing dataset', making both the resource and operation explicit. The use case 'start tracking something new in an existing dataset' and the concrete natural-language example further clarify its scope and distinguish it from sibling tools like update_column, remove_column, and create_dataset.

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 gives a clear trigger condition: 'Use this when the user wants to start tracking something new in an existing dataset', including a sample user request. It does not explicitly name alternatives or say when not to use this tool, but the situational guidance is unambiguous enough for selection.

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