query_metric_data

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

No annotations are provided, so the description carries the full burden of behavioral disclosure. It describes the tool as a query operation, implying it's likely read-only, but doesn't explicitly state this or mention other behavioral traits like authentication needs, rate limits, error handling, or data format specifics. For a tool with 5 parameters and no annotations, this is a significant gap in 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?

The description is well-structured and appropriately sized, with a clear purpose statement followed by parameter explanations and return information. Every sentence adds value, and there's no redundant or verbose content. However, it could be slightly more front-loaded by emphasizing the core purpose more prominently, but overall it's efficient and easy to parse.

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 complexity (5 parameters, no annotations, no output schema), the description is moderately complete. It covers the purpose and parameters well, but lacks details on behavioral aspects like read-only status, error cases, or output structure beyond a high-level 'List of metric data points'. For a query tool with no structured output schema, more information on return values would enhance completeness, but it meets a baseline level.

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 description adds substantial meaning beyond the input schema, which has 0% description coverage. It explains each parameter's purpose and provides examples (e.g., 'query: Metric query in MQL format (e.g., "CpuUtilization[1m].mean()")', 'start_time: Start time in ISO format (YYYY-MM-DDTHH:MM:SSZ)'). This compensates fully for the schema's lack of descriptions, making the parameters clear and actionable.

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's purpose: 'Query metric data for a time range using MQL.' It specifies the verb ('query'), resource ('metric data'), and method ('using MQL'), which is specific and actionable. However, it doesn't explicitly differentiate from sibling tools like 'list_metrics' or 'search_logs', which might also retrieve metric-related data, so it falls short of a perfect score.

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 provides no guidance on when to use this tool versus alternatives. It mentions using MQL for querying metric data over a time range, but doesn't specify prerequisites, exclusions, or compare it to sibling tools like 'list_metrics' (which might list available metrics) or 'search_logs' (which might handle log-based queries). This lack of contextual usage advice leaves the agent with minimal direction.

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