data_describe

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

Annotations already declare readOnlyHint and idempotentHint. The description adds valuable behavioral context beyond annotations by stating that row_count is the true dataset size and that sample rows are examples, not the data, preventing misuse. No contradiction with annotations.

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 output spec is front-loaded and the usage guidance is dense and useful. The keyword block, including Spanish duplicates, adds length but supports retrieval and does not introduce redundancy or filler.

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?

The description adequately covers output shape, when to invoke the tool, and how it relates to data_query, especially given there is no output schema. The main gap is the unstated semantics of the `name` parameter, which is already penalized under parameter semantics.

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 only parameter, `name`, has no schema description and schema coverage is 0%. The description never explains that `name` is the table or dataset identifier or how it should be supplied, leaving the agent to infer this from the tool name and surrounding context.

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 first line precisely specifies the tool's output: schema, row_count, per-column profile with named statistics, and 5 sample rows. The instruction to call this before data_query clearly distinguishes it from the data_query sibling, which handles aggregation.

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 states when to use the tool: before answering anything about a table, never guessing column names, types, or row counts. It also names the alternative, data_query, for aggregation and warns against summarizing from sample rows.

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