Gemini Email Subject Generator MCP

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 mentions AI subject generation but lacks critical details: whether this is a read-only or mutating operation (implied mutation from 'send'), authentication requirements, rate limits, error handling, or what happens upon success/failure. This is inadequate for a tool that likely performs external communication.

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 a single, efficient sentence with zero wasted words. It front-loads the core purpose and key feature without unnecessary elaboration, making it easy to parse quickly.

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 tool with 5 parameters, no annotations, and no output schema, the description is insufficient. It lacks behavioral context (e.g., side effects, permissions), doesn't explain the relationship with the sibling tool, and provides minimal guidance on usage. The AI subject generation is noted but not elaborated, leaving gaps in understanding the tool's full scope.

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 100%, so the schema fully documents all parameters. The description adds minimal value beyond the schema—it implies 'subjectPrompt' is used for AI generation but doesn't explain how this interacts with other parameters or provide usage examples. Baseline 3 is appropriate as the schema does the heavy lifting.

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 action ('Send an email') and specifies a key feature ('with AI-generated subject using Gemini Flash 2'), which distinguishes it from generic email tools. However, it doesn't explicitly differentiate from the sibling tool 'generate-thinking', which might be related but has an unclear relationship.

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 no guidance on when to use this tool versus alternatives, prerequisites, or constraints. It mentions AI-generated subjects but doesn't specify scenarios where this is beneficial or when manual subjects might be preferred. No exclusions or sibling tool comparisons are included.

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