get_async_query_result

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

No annotations are provided, so the description must carry the full burden of behavioral disclosure. It mentions polling for results but does not describe what happens if the query is still running, timeout behavior, error handling, or whether the tool blocks. This is insufficient for a polling tool.

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 concise and well-structured: a one-line summary, then a clear explanation of the async query flow, and a separate Args section for the parameter. Every sentence adds value without fluff.

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 has one parameter and an output schema exists, the description covers purpose and parameter semantics adequately. However, it could be more complete by detailing polling behavior (e.g., retry logic, timeout).

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?

With 0% schema description coverage, the description adds meaning by explaining that query_id is the key returned by an async SQL Lab query. It provides context beyond the schema's type and required flag. However, it could be more detailed about format or source.

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 fetches results of an async SQL query by its query ID. It uses a specific verb 'Fetch results' and resource 'async SQL query', and the context distinguishes it from synchronous query tools like run_sql.

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 explains that this tool is used after an async SQL Lab query returns a query ID, and to poll for results once the query completes. While it gives clear context for using the tool, it does not explicitly state when not to use it or name alternatives, but the sibling tool list provides that context.

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