describe_table

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

No annotations are provided, so the description carries the full burden, yet it says nothing about read-only nature, behavior for unknown tables, or error cases. It only asserts the shape of the returned 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?

One short sentence with no filler and the key concept front-loaded. It is fragmentary and slightly under-specified rather than padded.

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?

For a simple single-parameter read tool this covers the essential output (columns and types), but with no annotations and no output schema, the description should also say what a failure looks like and confirm there are no side effects.

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 0% with a single 'table' string parameter, so the description must compensate. The examples ('issues', 'cases') give the agent real domain values to pass, which is genuinely useful, but there is no note on naming conventions, case sensitivity, or what happens for invalid names.

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?

States a specific resource (columns and types) tied to a results-store table, with concrete table examples ('issues', 'cases'). It is distinguishable from sibling list_tables, though the verb is only implied by the name rather than stated in the description.

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?

No guidance on when to call this versus inspect_dataset, list_tables, or run_query. An agent must infer that this is the schema-introspection step before querying.

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