describe_table

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

Beyond the readOnlyHint/idempotentHint annotations, the description discloses the exact return format (Markdown with row count, columns, semantic definitions, join keys, caveats) and error behavior, including the error message with available tables. This is rich behavioral context.

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 well-structured with clear sections (main description, Args, Returns, Examples). Every sentence serves a purpose, and it is front-loaded with the core functionality. Length is justified by the depth of useful guidance.

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 parameter, read-only, with output schema), the description is complete: it explains what it does, when to use it, what it returns, and error handling. No critical information 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?

The schema description already covers the parameter ('Table name, e.g. 'orders'. Case-insensitive.'). The description's Args section repeats the same information without adding new meaning, so baseline 3 for high schema coverage is appropriate.

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 uses a specific verb and resource: 'Describe one table: columns, types, null rates, distinct counts, examples.' It clearly differentiates from siblings by noting when not to use it and recommending profile_column for single-column distribution.

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?

Explicit guidance is provided: 'Call this before writing a query against an unfamiliar table' with concrete use cases and a non-use case that names an alternative tool (profile_column). This clearly explains when to use this tool versus siblings.

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