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

Annotations already indicate readOnly and idempotent, so the description doesn't repeat that. It adds context about provenance and the listing-unavailable behavior, which is useful, but doesn't detail error cases or output format beyond what annotations imply. No contradiction.

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 reasonably concise, with the main use case front-loaded. The second sentence about auth limitation is somewhat lengthy but necessary. No redundant fluff.

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 a single optional parameter, complete schema coverage, an output schema, and read-only annotations, the description covers the important usage context: when to call it, how to obtain the parameter, and when listing is unavailable. Nothing critical is missing.

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%, with the dataframe parameter documented as optional and controlling list vs. describe. The description reinforces this by explaining how to obtain the name from spillover, but adds minimal new meaning 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 lists dataframes or describes one, with columns, row counts, and provenance. It distinguishes from siblings by focusing on schema/provenance inspection before queries, but could be more explicit about being a metadata-only tool compared to brapi_dataframe_query.

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 this tool after a spillover, before writing the first brapi_dataframe_query, and explains when listing is unavailable (shared HTTP endpoint without auth) with a fallback to pass a known name. This is strong guidance.

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