get_summary

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

The description clearly indicates a read-only operation without side effects. Since no annotations are provided, the description adequately covers the behavioral context by listing the returned data. It could mention any required authentication or data freshness, but remains sufficient for a simple stats tool.

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 sentence of 13 words, front-loading the core action and output. Every word contributes meaning, with no redundancy or filler.

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 simplicity (0 parameters and an output schema present), the description provides sufficient context about what the tool returns. It could hint at the output structure, but the output schema likely covers that. The description is adequate for an agent to understand the tool's role.

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 tool has zero parameters, and the input schema is empty. The description does not need to add parameter information. Per guidelines, 0 parameters receives a baseline score of 4.

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 retrieves inbox statistics, specifying the exact fields: total messages, unread count, and messages from the last 7 days. It distinguishes itself from sibling tools by focusing on aggregated data rather than individual message operations.

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 use when needing summary statistics, but does not explicitly state when to use this tool versus alternatives like list_messages or search. No guidance on prerequisites or exclusions is provided, leaving room for misuse.

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