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

The description adds valuable context beyond the annotations (readOnlyHint: true, destructiveHint: false) by specifying the scope of returned data ('both system labels... and user-created labels with message/thread counts') and the authentication requirement ('for the authenticated user'). While annotations cover safety, the description enhances understanding of what the tool does without contradicting them, though it doesn't detail rate limits or exact output format.

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 front-loaded with the core purpose in the first sentence, followed by additional details and usage guidance, with no wasted words. Every sentence adds value—defining the return data and explaining when to use the tool—making it efficient and well-structured for quick comprehension by an agent.

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 low complexity (0 parameters, read-only, non-destructive) and the presence of an output schema, the description is complete enough. It covers the purpose, usage context, and behavioral details, aligning with annotations and sibling tools, without needing to explain return values since the output schema handles that, making it fully adequate for agent selection.

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?

With 0 parameters and 100% schema description coverage, the description compensates by explaining the tool's behavior and output context. It clarifies that no inputs are needed and adds semantic meaning about the returned data (label types and counts), which the schema alone doesn't provide, making it more informative than the baseline of 3 for high schema coverage.

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 specific action ('List all Gmail labels') and resource ('for the authenticated user'), distinguishing it from siblings like 'create_label' or 'delete_label' by focusing on retrieval rather than modification. It explicitly mentions what is returned ('both system labels... and user-created labels with message/thread counts'), making the purpose unambiguous and well-defined.

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 on when to use this tool ('to discover label IDs needed for add_labels, remove_labels, or search_email queries'), directly naming alternative tools and clarifying its role in the workflow. This helps the agent understand the tool's context and when to select it over other options, such as for ID discovery rather than label management.

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