search_metric

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

Since no annotations are provided, the description carries the full behavioral burden. It discloses non-obvious behaviors: camel-case keyword splitting, fuzzy namespace matching, match_mode AND/OR logic, and prefer_namespaces sorting. These details give an agent a realistic model of how search results are filtered and ranked.

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 text is front-loaded with the core purpose, then organized under Args and Returns headings. Each line provides necessary operational detail (examples, defaults) without excessive fluff, making the definition scannable and efficient for an LLM to consume.

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?

Although an output schema exists, the description still summarizes return contents (name, description, namespace, available labels) and fully documents all five parameters, defaults, and matching/sorting behaviors. With no annotations and no schema-level descriptions, nothing essential is missing for correct invocation.

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 0%, so the description compensates completely. Every parameter—keywords, namespace, limit, match_mode, prefer_namespaces—has a format explanation, examples, and defaults. For instance, keywords is shown with ['CPU', 'Usage'] and the camel-case splitting rule, and match_mode explicitly defines 'and'/'or' and the default.

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 statement: '搜索可用的 ZStack 监控指标' (search available ZStack monitoring metrics). It clearly identifies a search operation over a distinct resource (monitoring metrics), separating it from sibling tools like search_api or get_metric_data.

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?

Usage is implied through the name and purpose—searching available metrics before fetching data—but no explicit guidance is provided about when to choose this tool over siblings such as get_metric_data or get_metric_summary. There are no when-not or alternative conditions, leaving an agent to infer the positioning.

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