MCP Datadog Server

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 mentions the tool fetches 'all custom metrics by hourly average' and specifies parameter usage, but lacks critical details: it doesn't indicate whether this is a read-only operation, what permissions are required, if there are rate limits, or the expected output format. For a tool with zero annotation coverage, 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 highly concise and well-structured: it starts with the core purpose, then immediately provides usage rules for parameters. Both sentences are essential and front-loaded, with no redundant information. It efficiently communicates key details without any waste.

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 complexity (fetching metrics with resolution options), the description covers the purpose and parameter usage adequately. However, with no annotations and no output schema, it lacks details on behavioral traits (e.g., safety, permissions) and return values. The description does not fully compensate for these gaps, making it only minimally complete for effective agent use.

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 input schema has 0 parameters with 100% coverage, meaning there are no parameters documented in the schema. The description compensates by explaining that 'month' and 'day' parameters are used for data resolution, with one required and exclusive. This adds meaningful semantic context beyond the empty schema, though it doesn't specify parameter types or formats, keeping it from a perfect score.

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: 'Get all custom metrics by hourly average.' It specifies the resource (custom metrics) and the operation (get by hourly average), making the intent unambiguous. However, it doesn't explicitly differentiate from sibling tools like 'get_metric' or 'get_metrics_v1/v2', which focus on different metric aspects, 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 clear usage guidance: 'Use the month parameter to get a month-to-date data resolution or use the day parameter to get a daily resolution. One of the two is required, and only one of the two is allowed.' This specifies when to use each option and their exclusivity. However, it doesn't mention when to use this tool versus alternative metric-fetching tools in the sibling list, such as 'get_metric' or 'get_metrics_v1/v2', leaving some contextual gaps.

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