query_dataframe

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

With no annotations, the description must carry the burden. It mentions using a 'built-in conservative planner,' which hints at cautious behavior, but does not disclose other traits like read-only nature, error handling, or rate limits. The output schema exists but is not described. Adequate but leaves gaps.

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 wasted words. The primary action is front-loaded in the first sentence, and the second provides a crucial usage hint. Excellent efficiency.

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's simplicity and the presence of an output schema, the description covers the core purpose and usage. However, it omits details about parameter semantics, possible question types, and behavioral nuances, leaving some gaps for an AI agent.

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 should compensate, but it does not explain the parameters ('question', 'dataset_id'). While parameter names are somewhat self-explanatory, the description adds no further meaning, formatting hints, or examples. Below the baseline of 3.

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 purpose: 'Answer a natural-language question about the dataframe.' It also distinguishes itself from sibling tools by mentioning that for complex analysis, one should use execute_analysis_plan.

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 explicitly advises when to use this tool (simple natural-language questions) and when not (complex analysis), naming the alternative: 'call execute_analysis_plan with a typed plan.'

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