Fetch Gmail label IDs and names with system labels first, enabling agents to apply or remove labels by ID or exact name.
List Gmail labels (id, name, type system/user): system labels first, then the user's.
label_* and unlabel_* take these IDs (or the exact names).
| Name | Required | Description | Default |
|---|---|---|---|
No arguments | |||
| Name | Required | Description | Default |
|---|---|---|---|
| result | Yes |
Changes observed during successful MCP inspections.
v0.1.0Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
While annotations cover safety (readOnlyHint), the description adds a key behavioral trait: the specific order of results ('system labels first, then the user's'), which helps the agent parse the output.
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 definition is brief, clear, and front-loads the most critical information regarding the structure and output format.
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?
For a simple retrieval tool with no input parameters, the definition is maximally complete as it describes the output structure and its relationship to the rest of the API.
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?
Although the schema is empty (100% coverage means no description contribution is strictly necessary), the description provides semantic value by explaining the role of the IDs and names in the context of the related labeling functions.
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 is highly specific, clearly stating the resource ('Gmail labels'), the fields returned ('id, name, type'), and the organizational schema ('system/user') used in the output.
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?
It provides explicit usage context by stating that the identifiers returned by this tool are the required inputs for other sibling tools ('label_* and unlabel_*').
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.