query-metrics

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

With no annotations, the description carries full weight. It states the tool returns data points with timestamps, which is helpful. However, it lacks details on rate limits, error behavior, pagination, or any destructive implications. The description is adequate but not comprehensive.

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 concise (4 sentences) and front-loaded with the core purpose. The second sentence is slightly marketing-like but does not detract significantly. Every sentence adds value.

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 3-parameter tool with no output schema, the description covers the essential: purpose, query syntax, and return type. However, it omits details like response format (e.g., array of data points) or potential limitations (e.g., query range limits). It is adequate but not fully complete.

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 parameters are already documented. The description adds value by providing query syntax examples and real-world use cases ('request rate for web service'), which helps the agent understand parameter semantics beyond the schema descriptions.

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 it queries time-series metric data from Datadog, provides concrete examples ('CPU usage over last hour'), and gives query syntax, distinguishing it from sibling tools like get-metrics or get-metric-metadata which likely list metrics rather than query data points.

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 usage for metric queries and gives examples, but does not explicitly state when to use this tool versus alternatives like get-metrics or search-metric-volumes. No when-not-to-use or comparison is provided.

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