Gmail MCP Server

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

With no annotations provided, the description carries the full burden of behavioral disclosure. It effectively describes the tool's behavior as a mutation operation (sending an email), includes prerequisites (authentication, draft_id, user confirmation), and emphasizes the critical safety requirement (explicit user confirmation). However, it lacks details on potential side effects (e.g., email delivery status, error handling) or rate limits, which would enhance transparency further.

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 with sections (Prerequisites, Args, Returns, Example usage, IMPORTANT) and front-loaded key information. Most sentences earn their place by providing critical guidance, though the example usage could be slightly condensed without losing clarity. Overall, it is appropriately sized and organized for the tool's complexity.

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 complexity (a mutation with safety-critical requirements), no annotations, and no output schema, the description is complete. It covers purpose, usage guidelines, behavioral traits (including prerequisites and safety warnings), parameter semantics, and return values, providing all necessary context for an AI agent to use the tool correctly and safely.

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?

The schema description coverage is 0%, so the description must fully compensate. It clearly explains the single parameter 'draft_id' as 'The ID of the draft to send,' specifying its source ('from send_email_reply()') and purpose. This adds essential meaning beyond the bare schema, making the parameter's role and requirements explicit.

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 ('send a previously created draft email') and resource ('draft email'), distinguishing it from sibling tools like 'send_email_reply' (which creates drafts) and 'get_email' (which retrieves emails). It explicitly mentions the confirmation requirement, making the purpose distinct 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 ('after explicit user confirmation to send the email') and when not to use it ('Never call this function without explicit user confirmation'). It also lists prerequisites (authentication, draft_id from send_email_reply, user confirmation) and references the sibling tool 'send_email_reply' for creating drafts, offering clear alternatives and context.

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