update_label

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

The description adds the important behavioral note to ask for user confirmation before executing, which is beyond the annotations that already indicate destructiveness. However, it does not detail potential side effects beyond modification.

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?

Four concise sentences, front-loaded with purpose and usage guidelines. No redundant or unnecessary information.

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 and no output schema, the description sufficiently covers the tool's purpose, usage, and behavioral requirements. It could optionally mention return values, but is otherwise complete.

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 coverage is 100%, so the input schema already provides full parameter descriptions. The description mentions partial updates but does not add significant new meaning beyond what is in the schema.

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 modifies an existing label and supports partial updates. It explicitly distinguishes from create_label and update_annotation, making it easy for an AI agent to select the correct tool.

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 provides explicit guidance: use when modifying an existing label, do not use for creating labels or annotations, and ask user for confirmation before executing. This covers both when-to-use and when-not-to-use with alternatives.

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