cross_notebook_query

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

No annotations are provided, so the description carries the burden. It mentions rate limits for all=True and describes the output format. However, it does not explicitly state that the tool is read-only or disclose other behavioral traits like error handling or synchronization.

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 concise: a brief summary followed by an Args list. Every sentence adds value, with no fluff. It is well-structured and front-loaded with the core purpose.

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 there is an output schema (unseen), the description covers the main aspects: query purpose, parameter options, and output format. It lacks details on error handling, authentication, or concurrency, but is sufficient for basic use.

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 the description must fully document parameters. It describes all four parameters with examples (e.g., 'AI Research, Dev Tools') and their roles. It adds meaning beyond the schema types, though it omits explicit mention of defaults for notebook_names and tags.

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 verb 'query', resource 'multiple notebooks', and output 'aggregated answers with per-notebook citations'. This distinguishes it from sibling 'notebook_query' which likely queries a single notebook.

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 explains how to specify notebooks (by name, tags, or all=True) and includes a caution for all=True about rate limits. However, it does not explicitly state when to use this tool versus alternatives like notebook_query, though it is implied.

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