verify_dql

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

Discloses key behavior: executes with limit 0, returns ok or error. No annotations provided, so description carries full burden. It could further state that it is non-mutating, but the validation nature implies this.

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?

Single, front-loaded sentence with no unnecessary words. Efficiently conveys purpose, execution method, and return value.

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?

Reasonably complete for a simple validation tool: explains no-data-return and return format. Could detail the structure of the validation error, but not essential.

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% with description of parameter 'query'. Description adds no additional insight beyond the schema; baseline score of 3 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?

Description clearly states the verb 'Validate', specifies the resource 'DQL statement', and explains it runs with limit 0 to avoid returning data, distinguishing it from execute_dql.

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?

Provides clear context for use (validation without data return) but does not explicitly mention when not to use it (e.g., when you need results, use execute_dql), though sibling tools make this implied.

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