input_get_length

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 clearly indicates a read-only length fetch and defines what is counted, but it does not mention return type, error behavior, or whether input_path must reference an existing Input dataset.

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?

A single, front-loaded sentence with no filler. Every word contributes to explaining the operation and its result.

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 getter with no annotations and no output schema, the description is nearly complete: it states the resource, the measured quantity, and the result. Minor gaps like exact return type and invalid-path behavior are acceptable given the tool's low complexity.

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 schema provides only the parameter name input_path with type string and no description, so schema coverage is 0%. The description partially compensates by implying input_path identifies the Input dataset, but it does not explicitly document the path format or any constraints.

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 verb ('Get'), a clear resource ('the Input dataset'), and the exact result ('the number of data points'). It clearly distinguishes this from sibling tools like input_get_data or input_get_df, which retrieve data rather than its length.

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 no guidance on when to use this tool versus alternatives such as output_get_length, transform_get_length, or setup_get_length. There is no mention of context, prerequisites, or exclusions, so an agent must infer appropriateness from the name alone.

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