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

No annotations are provided, so the description must cover behavioral traits. It mentions code validation before execution and error reporting. However, it does not disclose potential risks (e.g., code injection, resource consumption) or details about the execution environment. This is adequate but not comprehensive.

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 three sentences, front-loaded with the main purpose, and each sentence is meaningful. No superfluous text.

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 parameter count (4), required params (1), high schema coverage (100%), and presence of output schema, the description is reasonably complete. It explains execution behavior and use cases. Could add more on return values or error handling specifics.

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 100%, so the baseline is 3. The description adds context about execution modes (jupyter vs panel) but mostly echoes the schema. It does not provide new meaning beyond what the schema already offers.

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: 'Display Python code visualization in a browser.' It specifies verb (display), resource (Python code visualization), and scope (browser via Panel). This distinguishes it from sibling tools which are mostly get/list operations for docs or panel objects.

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 explicitly says 'Use this tool whenever the user asks to show, display, visualize data, plots, dashboards, and other Python objects.' This provides clear context and use cases. However, it does not explicitly mention when not to use it or alternative tools.

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