MCP Datadog Server

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

Annotations are minimal (no readOnlyHint, destructiveHint, etc.), so the description carries the burden. It adds valuable behavioral context by disclosing the rate limit ('300 requests per hour'), which is not inferable from annotations. However, it lacks details on output format, error handling, or side effects, leaving some gaps.

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 two sentences, front-loaded with the core purpose and followed by rate-limiting info. It is efficient with minimal waste, though it could be slightly more structured (e.g., separating purpose from constraints).

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 has 0 parameters, no output schema, and minimal annotations, the description is moderately complete. It covers purpose and a key constraint (rate limit), but lacks details on output (e.g., what metrics/timeseries are returned), error cases, or how it differs from siblings, making it adequate but with clear gaps.

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, so no parameter documentation is needed. The description does not mention parameters, which is appropriate. A baseline of 4 is applied as it avoids redundancy and focuses on other aspects, though it could note the lack of parameters explicitly.

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 states the tool 'aggregates spans into buckets and computes metrics and timeseries', which clarifies the verb (aggregate/compute) and resource (spans). However, it does not distinguish this from sibling tools like 'aggregate_logs_analytics' or 'aggregate_rum_analytics', leaving the scope vague regarding what 'spans' specifically refer to (e.g., APM spans vs. other types).

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?

No guidance is provided on when to use this tool versus alternatives. The description mentions it is an 'API endpoint' and rate-limited, but does not specify use cases, prerequisites, or contrast with siblings like 'search_spans_events' or other aggregate tools. This leaves the agent without context for selection.

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