query_dameng

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

With no annotations, the description carries full burden. It mentions DML (modification), which hints at destructive potential, but does not disclose side effects, authentication requirements, or error behavior. This is adequate but not detailed.

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, concise sentence that includes the key elements: action, resource, supported operations, and output type. No superfluous words.

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 low complexity (2 parameters, no output schema, no nested objects), the description is largely complete. It covers the essential purpose and return type. However, it could mention limit default or error handling for a truly complete picture.

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%, so description adds value by clarifying the tool supports both SELECT and DML, which is not explicit in the schema's parameter descriptions. The description also notes the return type '结构化结果' (structured results), providing context beyond the schema.

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 verb 'execute' and resource 'Dameng database SQL queries', explicitly listing supported SQL types (SELECT/DML). This distinguishes it from sibling tools describe_table and list_tables, which focus on metadata.

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 is given on when to use this tool versus the sibling tools. For a tool that executes arbitrary SQL, it would be helpful to note that it's for general queries, while describe_table and list_tables are for schema inspection.

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