describe_table

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

With no annotations provided, the description carries the full burden. It discloses the interpretive behavior of the `unit` field, explaining that it distinguishes gross/net and cents/dollars, and instructs the agent to say 'unknown' rather than guess when the unit is undocumented. This adds valuable context beyond the schema, but it doesn't cover other potential behaviors like error handling or access requirements.

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 front-loaded with the purpose, followed by usage guidance and a clearly structured Args section. Every sentence adds information, from the unit field warning to the parameter explanations, with no filler. It's concise yet complete.

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 covers the essential context for using this tool: when to call it, what to expect (purpose, grain, owner, status, columns), and the key interpretation caveat about the unit field. Since an output schema is present, it doesn't need to enumerate return values. It's complete for the tool's complexity.

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 schema has no descriptions for the parameters (0% coverage), but the description compensates fully with an Args section. For `table`, it provides an example of a fully qualified name; for `include_columns`, it explains when to set it to False. This gives the agent the needed semantic detail.

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 starts with a clear, specific statement: 'Get the full meaning of one table: purpose, grain, owner, status, and columns.' This immediately identifies the verb, resource, and scope, distinguishing it from sibling tools like search_tables, which searches across tables, and trace_lineage, which follows relationships.

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 provides a clear usage rule: 'Call this before using any table you have not already described in this conversation.' It also gives conditional guidance for include_columns, saying to set False when only purpose, owner, and status are needed. However, it doesn't explicitly mention alternative tools or describe when not to use it beyond the already-described condition, so it lacks the full 5-level criteria.

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