summarize_email

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

With no annotations, the description must disclose behavioral traits. It states that the tool uses the connected LLM and requires sampling support, which is a notable dependency. However, it does not explicitly confirm that the tool is read-only or describe error behavior if the email is not found or sampling is unsupported.

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 two sentences, front-loaded with the core action and output fields. The second sentence about sampling support is essential context and earns its place. There is no redundant or unclear wording.

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?

The description covers the core purpose, output content, and a key dependency. Since there is no output schema, it appropriately specifies that the summary includes subject, sender, and body. It could mention the return format or failure handling, but for a one-parameter tool this is reasonably 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?

The single parameter emailId is fully documented in the schema, including guidance to use search_emails or get_latest_emails to find IDs. The tool description adds no additional parameter semantics beyond what the schema already provides, but the 100% schema coverage makes this adequate.

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 tool produces a concise summary of an email's subject, sender, and body using the connected LLM. This specific verb+resource pairing distinguishes it from sibling retrieval tools like get_email and search_emails, making the purpose unmistakable.

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 implies this tool should be used when a concise summary is desired, but it does not explicitly state when to use it versus alternatives like get_email or suggest_reply. It mentions a prerequisite of MCP client sampling support, but provides no when-not-to-use guidance.

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