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

No annotations are provided, so the description carries full burden. It mentions blocking and timeout but does not specify what happens on timeout (e.g., returns null or error). It also doesn't clarify if the message is consumed or remains in the inbox. This ambiguity affects transparency.

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?

Two sentences, front-loaded with key information (blocking, matching, timeout). No superfluous words. Efficient and clear.

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?

Captures purpose, usage timing, and parameter semantics well. However, lacks detail on timeout behavior and return format (no output schema). Also doesn't address whether the message is consumed or remains. This leaves gaps for an AI agent.

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 coverage is 100%, so baseline is 3. The description adds value by explaining the match filter syntax (e.g., 'subject:term') and the context of the 'since' parameter. It also notes the default timeout. This enhances understanding beyond the schema.

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 (blocks/wait), the resource (matching message in inbox), and the result (returns full detail). It also provides context for usage, distinguishing from sibling tools like get_message or list_messages.

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 explicitly advises to call this after triggering an email flow, giving clear situational guidance. It does not explicitly state when not to use it, but the context is sufficient for an AI agent to infer appropriate usage.

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