auto_eda

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

With no annotations, the description must disclose behavioral traits. It mentions generating plots and a narrative summary but lacks details on side effects (e.g., data mutation), performance implications, or limitations. The description is adequate but not thorough.

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 concise with two sentences, front-loading the purpose. Every word adds value, with no redundancy.

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 complexity (multi-panel dashboard, narrative summary) and the presence of an output schema, the description is fairly complete. It covers key outputs but does not detail the return format or dashboard layout.

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 coverage is 100%, so the description need not add much. It mentions 'maximum number of plots' and the inclusion of distributions/correlations, but these are already covered by the schema. No additional semantic insight beyond the schema.

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 runs a complete exploratory data analysis in one call, listing specific outputs (distributions, correlations, categorical breakdowns, narrative summary) and returns a dashboard. This distinguishes it from siblings like analyze_data or statistical_test.

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 use for quick, comprehensive EDA but does not explicitly state when to use this tool vs alternatives or when not to use it. No guidance on prerequisites or exclusions.

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