Read a Rootr whiteboard

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

Annotations already mark the tool as read-only and non-destructive, so the description does not need to restate these. The description adds value by specifying the exact data returned (shapes and edges), which helps the agent understand the tool's behavior beyond the annotations.

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?

Two sentences, both essential. The first clearly states the tool's action and output, while the second provides critical usage guidance. No superfluous content.

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 simplicity (one required parameter, no output schema, clear annotations), the description covers the core aspects: what it reads, what it returns, and when to use it. It could optionally mention that the output is a JSON object, but it is not strictly necessary.

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% for the single parameter whiteboardId; the description does not add new parameter-level details beyond what the schema provides. Therefore, it meets the baseline expectation.

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?

Description uses a specific verb 'Read' and identifies the resource as a 'WHITEBOARD node'. It clarifies the return structure by mentioning 'full scene (shapes[] and edges[])', and implicitly distinguishes from sibling tools like rootr_update_whiteboard.

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?

Explicitly instructs to call this tool before rootr_update_whiteboard to obtain current shape/edge ids and content, providing clear and actionable usage context.

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