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

With no annotations, the description carries the full burden. It usefully discloses the Caddie constraint that select-column options must be requested from the user, which is real behavioral context. However, it omits permissions, reversibility, duplicate-name handling, and what the call returns for a mutation tool.

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?

Two compact sentences, front-loaded with the core operation and the column structure, followed by the caveat. No wasted prose.

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 mutation tool with a nested object and no annotations or output schema, the description covers structure and one key behavioral edge case but leaves gaps: return value, error/duplicate behavior, and whether existing rows are affected when adding a column.

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 67%, and the description adds value beyond it by spelling out the column object shape ({ name, type, options?, unique? }) and clarifying that options applies to select columns. The 'as in create_data_table' cross-reference helps an agent construct valid nested input.

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: 'Add a column to a data table', and clarifies the column shape by referencing create_data_table. This distinguishes it from siblings like add_rows or create_data_table, though it doesn't explicitly contrast when to extend an existing table vs. create a new one.

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?

Provides a meaningful constraint: for select columns, if a needed value isn't an option, ask the user rather than creating a near-duplicate. But there's no explicit guidance on when to use this vs. create_data_table or other column-related operations; usage is largely implied.

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