notebook_query

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

With no annotations provided, the description carries the full burden of behavioral disclosure. It describes the core function (querying existing sources) and mentions timeout behavior with environment variable fallback, but doesn't cover other important aspects like authentication needs, rate limits, error handling, or what the AI response format looks like. It adds some context but leaves gaps for a tool with 5 parameters.

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 efficiently structured with a clear purpose statement, usage guidelines, and parameter explanations in a bullet-like format. Every sentence earns its place, with no wasted words. The information is front-loaded with the most important guidance first.

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 complexity (5 parameters, no annotations, but with output schema), the description provides good coverage of purpose, usage guidelines, and parameter semantics. The presence of an output schema means the description doesn't need to explain return values. However, it could provide more behavioral context about the AI interaction aspect.

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 must compensate for the lack of parameter documentation in the schema. It provides clear explanations for all 5 parameters: notebook_id (Notebook UUID), query (Question to ask), source_ids (Source IDs to query with default behavior), conversation_id (For follow-up questions), and timeout (Request timeout with default logic). This adds substantial meaning 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?

The description clearly states the tool's purpose: 'Ask AI about EXISTING sources already in notebook.' It specifies the verb ('Ask AI about'), resource ('EXISTING sources'), and scope ('already in notebook'), and explicitly distinguishes it from sibling 'research_start' for finding new sources.

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 provides explicit usage guidelines: 'NOT for finding new sources' and 'Use research_start instead for: deep research, web search, find new sources, Drive search.' This clearly defines when to use this tool versus alternatives, including specific exclusions and named alternatives.

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