sql_query_v1_datasets__provider___dataset__query_post

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

With no annotations, the description discloses the sandboxed DuckDB environment and automatic table binding, but omits details on failure handling, rate limits, or result structure. The information is sufficient but not comprehensive.

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 brief and front-loaded with the endpoint, but it contains necessary information. It could be slightly more structured but avoids verbosity.

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 (nested body, multiple parameters, no output schema), the description lacks critical details about return format, error messages, or parameter usage, making it incomplete for an agent to use reliably.

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 low (33%), and the description adds no explanation of parameters like provider, dataset, or the body fields. It only hints at table reference binding, leaving most parameters semantically underspecified.

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 'Execute a SQL query against a dataset's parquet file' and specifies the endpoint, making the tool's purpose unmistakable. It distinguishes itself implicitly from cross-dataset queries by targeting a single dataset.

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 notes that only SELECT statements are allowed, but does not explicitly guide when to use this tool versus siblings like cross_dataset_query_v1_query_post. No when-not or alternative recommendations are provided.

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