notebook_query_start

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

Describes asynchronous behavior (returns immediately with query_id), timeout handling, and the polling workflow. No annotations provided, so description carries full burden. Does not mention auth or side effects, but it's a query initiation 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?

Concise, well-organized: intro sentence, when-to-use, workflow, then parameter list. Every sentence adds value with no 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?

Covers purpose, usage, workflow, and parameters. Output schema exists so return values are not required. Complete for an async query start tool.

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%, so description adds meaning for all 5 parameters: notebook_id, query, source_ids (default all), conversation_id (for follow-ups), timeout (default from env or 120s). Provides context beyond the bare 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 the tool starts an asynchronous notebook query for large notebooks that may timeout. Uses specific verbs ('Start', 'poll') and distinguishes from sibling tool notebook_query by specifying alternative use case.

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 advises when to use this tool instead of notebook_query: when querying notebooks with many sources (50+) or timeouts over 60 seconds. 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.