input_get_data

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

No annotations are provided, so the description carries the full burden. It adds 'as dictionary' and names the two flags, but does not explain what the imaginary flag does, how msb affects the returned data, or what the dictionary keys/values represent.

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?

Both sentences are compact and the main purpose is front-loaded. The second sentence is somewhat vague but still relevant, so no significant redundancy exists.

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?

With no output schema and no annotations, the description should clarify return structure, flag effects, and the required input_path, but it does not. The tool is usable at a basic level but an agent is left guessing about key behavioral details.

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 0%, yet the description only mentions msb and the imaginary flag without explaining their meaning or the required input_path parameter. This adds little beyond the raw schema, which already lists the enum values, defaults, and types.

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?

Clearly states a specific verb ('Get') on a specific resource ('Input dataset') and the return format ('as dictionary'), which helps distinguish it from siblings like input_get_df and input_get_fields. The mention of msb and imaginary flag gives extra specificity, though it does not explicitly name an alternative.

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 phrasing implies this tool is appropriate when a dictionary representation of an input dataset is needed, possibly with msb or imaginary toggles. It does not state when to prefer this over input_get_df, input_get_fields, or other input-read siblings, so guidance is only implied.

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