describe_table

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

No annotations are provided, so the description carries full behavioral disclosure. It specifies the returned fields (column name, type, nullable, default, comment), which is sufficient for a read-only metadata tool. It does not mention potential errors or performance, but the simplicity of the operation makes this acceptable.

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?

A single sentence that encapsulates the tool's purpose and output, with no wasted words. It is front-loaded with the main action and outcome.

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 metadata query tool with two parameters and no output schema, the description provides complete context: what the tool does, what parameters are needed, and what information is returned. No gaps remain.

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 100%, and the description adds extra semantic value: it notes that tableName is case-insensitive and that schema defaults to the current user's schema. This goes beyond the schema's basic description.

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 the tool's purpose: viewing column information (name, type, nullable, default, comment) of a Dameng database table. It effectively distinguishes from sibling tools 'list_tables' (lists tables) and 'query_dameng' (queries data).

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 usage for examining table schema, but does not explicitly state when to use it over siblings or provide alternative guidance. However, the context of sibling names makes the differentiation clear.

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