Server Quality Checklist

Tool Scores

• find_notebookA
  3.7/5.0
  Behavior2/5

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

  No annotations provided, so description bears full burden. It reveals partial match and case-insensitivity but omits behaviors like what happens on no match, multiple results, or error handling, leaving gaps for an agent.

  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?

  Two sentences with no wasted words. Purpose is stated first, then parameter documented. Efficient and front-loaded.

  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?

  For a simple search with one parameter and an existing output schema, the description is adequate but lacks details on return format, pagination, or edge cases (e.g., no results). Sibling differentiation is implicit only.

  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?

  Schema coverage is 0% (parameter has no description), so the description must add meaning. The line 'title: The title or partial title to search for.' clarifies the parameter's role beyond the type, which is helpful compensation.

  Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.

  Purpose5/5

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

  The description states a specific verb (find) and resource (NotebookLM notebooks) and clarifies partial match and case-insensitive search, distinguishing it from siblings like list_notebooks (lists all) or ask_notebook (Q&A).

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

  Usage Guidelines3/5

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

  The description implies usage when you have a title or partial title but does not explicitly state when to use this tool versus alternatives, nor provide exclusions or prerequisites.

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

• generate_quizA
  3.9/5.0
  Behavior3/5

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

  With no annotations provided, the description bears full responsibility for behavioral disclosure. It explains batching to bypass limits and support for question types, but does not mention side effects, authentication requirements, or whether the tool is read-only. This leaves some uncertainty, warranting a 3.

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

  Conciseness4/5

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

  The description is moderately sized and well-structured: a one-line summary, a behavioral note, and a bulleted Args list. It front-loads the main purpose. Slightly verbose due to the Args repetitions, but overall efficient. A 4 reflects good but not perfect conciseness.

  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?

  The description covers key aspects: parameters, batching, question types, and cumulative merging. However, it lacks details about the output JSON format (keys, structure), which would be helpful given no output schema. The language default ('es') is mentioned without explanation. Overall adequate but with notable gaps.

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

  Parameters5/5

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

  The input schema has 0% description coverage, so the description must compensate. It does so comprehensively by listing all 7 parameters with clear explanations, defaults, and permissible values (e.g., topic: 'all sources', difficulty: easy/medium/hard/mixed). This adds full meaning beyond the schema's bare types.

  Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.

  Purpose5/5

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

  The description clearly states the tool's purpose: generating a JSON quiz from a NotebookLM notebook's sources. It specifies the output format (JSON) and resource (notebook's sources). The verb 'generate' is specific, and the tool is distinct from sibling tools (ask, find, list). No ambiguity.

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

  Usage Guidelines3/5

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

  The description does not explicitly state when to use this tool versus alternatives. It mentions supporting multiple_choice and true_false question types, which provides some guidance, but no explicit context on when to choose this over siblings like ask_notebook. A 3 reflects the lack of direct usage instructions.

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

• list_notebooksA
  4/5.0
  Behavior3/5

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

  No annotations are provided, so the description carries full burden. It only describes the action and output, lacking details on side effects, permissions, or limits. However, as a read-only list operation, the description is minimally adequate.

  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 a single concise sentence that front-loads the verb 'List' and clearly conveys the action and output. No wasted words.

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

  Completeness4/5

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

  Given zero parameters and the existence of an output schema, the description sufficiently explains the tool's purpose and result. It could mention that it returns all accessible notebooks, but overall it is complete for a simple listing tool.

  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?

  There are zero parameters, so the schema coverage is 100% trivially. Per guidelines, baseline is 4. The description adds no parameter info but none is needed.

  Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.

  Purpose5/5

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

  The description clearly states it lists all available notebooks and specifies the returned fields (IDs, titles, source counts). This distinguishes it from sibling tools like ask_notebook, find_notebook, and generate_quiz.

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

  Usage Guidelines3/5

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

  The description implies use for getting an overview of notebooks but does not explicitly state when to use this tool versus alternatives or when not to use it.

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

• ask_notebookA
  4.1/5.0
  Behavior3/5

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

  No annotations are provided, so the description fully bears the transparency burden. It describes the tool as a read operation (get answer), non-destructive. However, it lacks details on authentication, rate limits, or the exact format of the answer. The output schema may compensate, but the description itself is minimal.

  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?

  Extremely concise and well-structured: one sentence for purpose followed by a numbered list for parameters. No unnecessary words.

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

  Completeness5/5

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

  Given the tool's simplicity (2 required params, no enums, output schema present), the description is complete. It explains what the tool does, the parameters needed, and how to get the notebook ID. The output schema likely covers return value details.

  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?

  The description adds valuable context beyond the schema by specifying that notebook_id is obtained via list_notebooks. The question parameter is simply restated. Schema coverage is 0%, so this addition is significant.

  Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.

  Purpose5/5

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

  The description clearly states the verb 'Ask a question' and the resource 'specific NotebookLM notebook', with a clear output 'get an AI answer based on its sources'. It distinguishes from siblings like list_notebooks, find_notebook, and generate_quiz by focusing on querying a notebook for answers.

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

  Usage Guidelines3/5

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

  The description implicitly guides usage by telling the user to use list_notebooks to find notebook IDs, but does not explicitly state when to use this tool vs alternatives or provide exclusions.

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