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chat_configure

Configure chat settings for NotebookLM notebooks by setting conversation goals, response length, and custom prompts to tailor AI interactions.

Instructions

Configure notebook chat settings.

Args: notebook_id: Notebook UUID goal: default|learning_guide|custom custom_prompt: Required when goal=custom (max 10000 chars) response_length: default|longer|shorter

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
notebook_idYes
goalNodefault
custom_promptNo
response_lengthNodefault

Output Schema

TableJSON Schema
NameRequiredDescriptionDefault

No arguments

Behavior2/5

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

No annotations are provided, so the description carries full burden. While 'Configure' implies a mutation/write operation, the description doesn't disclose important behavioral traits: whether this requires specific permissions, if changes are reversible, what happens to existing settings, rate limits, or what the response contains. The parameter documentation adds some context but doesn't address core behavioral transparency.

Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.

Conciseness5/5

Is the description appropriately sized, front-loaded, and free of redundancy?

The description is perfectly structured and concise: a clear purpose statement followed by well-organized parameter documentation. Every sentence earns its place, with no wasted words. The Args section uses bullet-like formatting that's easy to parse while remaining compact.

Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.

Completeness3/5

Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?

Given this is a mutation tool with no annotations but with an output schema (which handles return values), the description is moderately complete. The parameter documentation is thorough, but behavioral aspects (permissions, side effects, error conditions) are missing. For a configuration tool that presumably changes system behavior, more context about what 'configure' actually does would be helpful.

Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.

Parameters4/5

Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?

With 0% schema description coverage, the description must compensate - and it does well by explaining all 4 parameters in the Args section. It clarifies notebook_id is a UUID, goal has specific values (default|learning_guide|custom), custom_prompt requirements and constraints, and response_length options. 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.

Purpose4/5

Does the description clearly state what the tool does and how it differs from similar tools?

The description clearly states the tool's purpose with a specific verb ('Configure') and resource ('notebook chat settings'), making it immediately understandable. However, it doesn't distinguish this configuration tool from its many siblings (like notebook_create, notebook_describe, etc.), which would require explicit differentiation to earn a 5.

Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.

Usage Guidelines2/5

Does the description explain when to use this tool, when not to, or what alternatives exist?

The description provides no guidance on when to use this tool versus alternatives. With many sibling tools available (notebook_create, notebook_describe, notebook_query, etc.), there's no indication of when chat configuration is appropriate versus other notebook operations or what prerequisites might exist.

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

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