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

With no annotations, the description carries the burden of behavioral disclosure. It does convey that this is a statistics/read-style operation by listing computed outputs, but it does not mention any side effects, the effect of the optional hours parameter, or default behavior beyond what the schema independently documents.

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 compact sentence that front-loads the purpose and follows with concrete output facets. Every phrase adds information, and there is no redundant or filler content.

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 simple aggregate tool with one optional parameter and no output schema, the description adequately covers what the tool returns. The exact structural format of the distributions is unspecified, but this is not essential for an agent deciding whether and how to invoke the tool.

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% for the single optional hours parameter, so the schema fully documents the parameter. The description itself does not add any parameter-level meaning or examples, thus the baseline score of 3 is appropriate.

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 opens with a specific action and resource, "获取告警统计概览" (get alert statistics overview), and enumerates exact output dimensions: total count, distribution by type/severity/address, and latest alert time. This clearly separates it from sibling query_alerts, which implies listing individual alerts rather than aggregates.

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 for aggregate overviews through the word "统计概览", but it does not explicitly state when to choose this tool over query_alerts or when not to use it. The usage context is inferable but not stated.

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