describe_table

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

With no annotations, the description carries the full burden of behavioral disclosure. It lists the output fields but does not state whether the operation is read-only, what permissions are required, what errors might occur, or any side effects. This is a significant gap given the description is the only source of behavioral information.

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 a single, focused sentence that front-loads the key output properties and the parameter format. No word is wasted.

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 simplicity (one param, output schema present), the description covers the core intent and parameter format, but it lacks usage guidance and behavioral detail (e.g., read-only nature, permissions). These gaps prevent it from being fully self-contained, though the presence of an output schema mitigates the need to explain return values.

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 input schema provides only a 'table' string with no description. The description adds crucial meaning by specifying the acceptable formats ('dataset.table' or 'project.dataset.table'), guiding the agent on how to construct a valid value. This more than compensates for the 0% schema description coverage.

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 specifies that the tool returns schema, row count, size, partitioning, and clustering for a given table, and gives the exact format of the table identifier. This distinguishes it from siblings like list_datasets, list_tables, dry_run, and run_query, which perform different functions.

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 the tool is used for inspecting a table's metadata, but it provides no explicit guidance on when to choose this over siblings or any exclusions. There is no mention of prerequisites or scenarios where alternatives would be more appropriate.

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