get_shape

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

With no annotations, the description carries the full burden. It adds some behavioral context by calling it a 'Quick dimension check' and implying a read-only verification step, but it does not disclose potential error conditions, side effects, or whether it returns a value or prints to console. It is adequate but not rich.

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 two sentences, with the first stating the core purpose and the second giving usage context. Every word earns its place, and the most critical information (what it does) is front-loaded.

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 simple one-parameter tool with an output schema present, the description covers the core functionality and a key usage scenario. However, it lacks any guidance on the df_name parameter, which is a minor but notable omission. Overall, it is sufficiently complete for the tool's simplicity.

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, and the description does not mention the df_name parameter at all. The parameter's title 'Df Name' on the schema provides only minimal hint of its meaning. Since the description fails to compensate for the low schema coverage, this dimension is weak.

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 clearly states the tool's function: 'Get the number of rows and columns in the dataframe.' This is a specific verb+resource pairing that distinguishes get_shape from sibling tools like get_head, get_tail, and get_statistics, which focus on different aspects of the dataframe.

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 clear context for when to use the tool: 'Always run after filtering or dropping to verify not too many rows lost.' This gives actionable guidance, though it does not mention when not to use it or suggest alternatives, so it falls short of a perfect 5.

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