output_get_df

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

No annotations are provided, so the description carries the full burden of behavioral disclosure. It only states that the result is a DataFrame and that matrix indexing is optional, but it does not explain what msb and imag do, how m/n are interpreted, whether any mutation occurs, or what errors might arise. This is too thin for a tool with no structured behavioral hints.

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 a single, tightly worded sentence with no redundant phrasing. It front-loads the core operation and result type, which is good for quick selection. However, the compressed phrase 'optional matrix indexing' hides substantial parameter complexity, so the structure is efficient but slightly under-specified.

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 five parameters, an enum, a required output_path, no output schema, and no annotations, this one-sentence description is not enough. An agent cannot correctly determine what m, n, msb, and imag mean or how to call the tool reliably without additional domain knowledge. The description names the deliverable but not the operational specifics needed to invoke it correctly.

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%, so the description must compensate for five undocumented parameters. It only gestures at 'optional matrix indexing', which likely maps to m, n, msb, and imag, but does not define any of them. The required output_path parameter is never mentioned, leaving critical semantic gaps.

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 clear verb-resource-result relationship: 'Get Output dataset as DataFrame'. The phrase 'optional matrix indexing' adds a distinguishing feature and separates it from simpler sibling tools like output_get_data. However, it does not define what an 'Output dataset' is or how matrix indexing works, so it falls just short of full clarity.

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 usage: an agent should call this when it wants an Output dataset as a DataFrame. It does not explicitly contrast with siblings such as input_get_df, transform_get_df, or output_get_data, nor does it state when not to use this tool. The usage context is inferable but not explicitly guided.

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