MCP Datadog Server

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

With no annotations provided, the description carries the full burden of behavioral disclosure. It adds useful context: it specifies that the tool returns 'distinct' metrics volumes and notes that custom metrics from other products return 'null' for ingested volumes. However, it doesn't cover other behavioral aspects like rate limits, authentication needs, error handling, or response format, leaving gaps for a tool with zero annotation coverage.

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 appropriately sized and front-loaded: the first sentence states the core purpose, and the second adds a critical behavioral note. Both sentences earn their place by providing essential information without redundancy or fluff, making it 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 (a read operation with no parameters), no annotations, and no output schema, the description is moderately complete. It covers the purpose and a key behavioral trait (null returns for custom metrics), but lacks details on output format, error conditions, or integration with sibling tools. For a tool in a large set with many alternatives, more context would help the agent use it correctly.

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% description coverage, so the schema fully documents the lack of parameters. The description implies a 'given metric name' is required, but since there are no parameters in the schema, this is likely handled differently (e.g., via context or path). The description adds no parameter details beyond what the schema provides, but with 0 parameters, the baseline is high, and it doesn't mislead.

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: 'View distinct metrics volumes for the given metric name.' It specifies the verb ('View') and resource ('metrics volumes'), making the action explicit. However, it doesn't differentiate from sibling tools like 'get_metric' or 'get_metrics_v1/v2', which might also retrieve metric-related data, so it doesn't fully distinguish from alternatives.

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 that custom metrics from other products return 'null' for ingested volumes, which is a behavioral note but not usage guidance. There's no indication of prerequisites, context, or comparison to sibling tools like 'get_metric' or 'aggregate_*' analytics tools.

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