Google Workspace MCP Server

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

No annotations are provided, so the description carries the full burden of behavioral disclosure. It effectively describes the core functionality (adding/removing labels) and practical use cases (archiving/deleting via label changes), which is valuable. However, it lacks details on permissions required, error conditions (e.g., invalid label IDs), or rate limits, leaving gaps for a mutation tool with no annotation coverage.

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 well-structured and appropriately sized, with a clear purpose statement upfront, followed by usage examples and parameter details. Every sentence adds value, but the 'Args' and 'Returns' sections could be integrated more seamlessly into the narrative flow, slightly affecting readability.

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 moderate complexity (4 parameters, mutation operation) and no annotations, the description does a good job covering purpose, usage, and parameters. The presence of an output schema (indicated by 'Returns' in the description) reduces the need to detail return values. However, it could benefit from more behavioral context (e.g., error handling) to be fully 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 description coverage is 50% (only 'add_label_ids' and 'remove_label_ids' have descriptions in the schema). The description compensates by listing all parameters with brief explanations in the 'Args' section, adding meaning beyond the schema for 'user_google_email' and 'message_id'. This improves understanding, though it doesn't fully elaborate on parameter formats or constraints.

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 ('Adds or removes labels from a Gmail message') and resource ('a Gmail message'), distinguishing it from sibling tools like 'manage_gmail_label' (which likely manages labels themselves) and 'batch_modify_gmail_message_labels' (which handles multiple messages). The purpose is precise and unambiguous.

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 clear context for when to use this tool by explaining how to achieve specific outcomes (e.g., 'To archive an email, remove the INBOX label. To delete an email, add the TRASH label.'). However, it does not explicitly mention when NOT to use it or compare it to the sibling 'batch_modify_gmail_message_labels' for handling multiple messages, which would be helpful for differentiation.

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