notebook_query_start

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

No annotations provided. Description discloses async behavior, immediate return of query_id, and need to poll for results. However, it does not detail error handling, timeout behavior beyond the parameter, or authentication requirements. Still adds significant context.

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?

Front-loaded with purpose, then usage guidelines, workflow, and parameter list. No unnecessary words. Every sentence is valuable.

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?

Output schema exists, so return values are covered. Description explains the async workflow sufficiently. Could mention failure modes or cancellation, but overall complete for the tool's purpose.

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 description coverage is 0%, but description explains all five parameters: notebook_id (UUID), query (question), source_ids (default all), conversation_id (follow-up), timeout (default from env or 120). Adds meaning beyond raw 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?

Clearly states it starts an asynchronous notebook query for large notebooks that may timeout. Distinguishes from sibling notebook_query by noting that it is for notebooks with many sources (50+) and potential long response times.

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?

Explicitly tells when to use this tool instead of notebook_query (for large notebooks that may timeout). Provides a clear workflow: start, then poll notebook_query_status.

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