detect_column_types

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

With no annotations, the description carries the burden and does well: it discloses that detection is based on the first non-null value of each column, that the transformation is appended, and that the generated step is self-contained. It does not discuss side effects like overwriting existing types, but the core behavior is transparent.

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 sentences, front-loaded with the core purpose, then concise technical detail about the inference rule. Every word contributes; no filler or redundancy.

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?

Given the moderate complexity, lack of annotations, and no output schema, the description explains what happens, how the types are inferred, and that no external schema is needed. It doesn't mention return value or failure cases, but the transformation outcome is clear enough for practical use.

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%, and the description does not clarify the roles of sessionId or table. The description adds meaning about the table's data being used for inference, but it fails to compensate for the undocumented parameters, leaving the agent to guess their format and purpose.

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 ('auto-detect and apply') and names the resource (each column's type), clearly distinguishing it from siblings like set_column_data_type by covering all columns automatically. It also names the exact M function (Table.TransformColumnTypes), making the purpose unambiguous.

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 implies when to use: when you want automatic type detection without supplying a schema. However, it does not explicitly compare to alternatives like set_column_data_type or change_column_type, nor does it state when not to use this tool.

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