list_dataframes

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

With no annotations provided, the description carries the full burden. It clearly states the tool lists all loaded dataframes and their shapes, which is the core non-destructive behavior. It does not mention edge cases or that it has no side effects, but the simple read-only nature is adequately conveyed.

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 with no redundant wording; the first states the function, the second provides usage guidance. Efficient and 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?

Given the tool has no parameters and an output schema exists, the description sufficiently covers the purpose and usage context. It is complete for an agent to select and invoke this tool correctly among many siblings.

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 tool has zero parameters, so the description cannot add parameter semantics beyond the schema. The baseline of 4 applies as no parameter documentation is needed.

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 the specific verb 'List' with the resource 'all loaded dataframes' and clarifies the output includes their shapes. This distinguishes it from sibling tools like set_current_dataframe or merge_dataframes by focusing on inventory rather than modification.

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?

It advises checking available datasets before switching or merging, giving clear context for when to call this tool. It does not explicitly name alternative tools, but the suggested usage implies it is a precursor to operations like set_current_dataframe or merge_dataframes.

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