extract_data_insights

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

No annotations exist, so description carries full burden. It mentions output is sorted by severity and includes severity levels, but lacks info on destructive behavior, auth, rate limits, or performance constraints for large datasets.

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?

Three short paragraphs, each serving a purpose: purpose, output details, usage guidance. Information is front-loaded, but the structure could be tightened by merging usage hint into the purpose sentence.

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 output schema exists and no annotations, description adequately covers the tool's behavior. It explains insight categories, sorting, and prerequisite step. Missing slight nuance on behavior when optional columns are omitted, but overall complete.

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%, baseline 3. Description adds value by specifying that data comes from get_resource_data(), and clarifies the meaning of time_column and entity_column as temporal/entity identifier columns.

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 it extracts surprising findings (extremes, trends, outliers, inequality) and details the output structure (severity, headline, narrative). This distinctively differentiates it from sibling tools like compute_metrics or generate_data_narrative.

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?

Explicitly instructs to use AFTER data_profile() to identify time/entity columns, giving clear usage context. However, it does not explicitly mention when not to use or list alternative tools for other analysis needs.

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