superset_database_available_engines

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

With no annotations provided, the description carries the full burden. It clearly indicates a non-mutating read operation via 'List' and 'Returns available engines', which is sufficiently transparent for a zero-parameter, read-only tool. It does not overpromise or hide side effects.

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?

Three short sentences, all informative. The action is front-loaded ('List supported database engine types'), followed by return examples and a usage hint. No wasted words or redundancy.

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, zero parameters, and presence of an output schema, the description fully covers what it does, what it returns, and when to use it. Nothing essential 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 tool has zero parameters, so baseline is 4. The description adds nothing about parameters, but none are needed. Schema coverage is 100% and the empty schema is self-explanatory.

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 specific verb and resource: 'List supported database engine types for creating connections.' It clearly distinguishes this from sibling database tools by focusing on engine types rather than databases themselves, and includes concrete examples (PostgreSQL, MySQL, SQLite).

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 explicitly states its use case: 'Useful for selecting an engine when creating a new connection.' This gives clear context for when to use it, though it does not mention alternative tools or when not to use it.

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