MCP Datadog Server

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

No annotations are provided, so the description carries full burden for behavioral disclosure. It mentions aggregation into 'buckets of computed metrics and timeseries,' which implies a read-only analytical operation, but doesn't specify whether this is a heavy computation, requires specific permissions, has rate limits, or what the output format looks like. For a tool with zero annotation coverage, this leaves significant behavioral 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 a single, efficient sentence that directly states what the tool does without any fluff. It's front-loaded with the core action ('aggregate') and resource ('CI Visibility test events'), making it easy to parse. Every word contributes to understanding the tool's function.

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 and no output schema, the description adequately explains the basic purpose. However, without annotations or output schema, it lacks details on behavioral traits (e.g., computation intensity, permissions) and return format. For an aggregation tool that likely produces structured analytics data, more context on output expectations would be helpful, though the absence of parameters simplifies the overall context.

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, meaning there are no parameters to document. The description doesn't need to compensate for missing parameter info, so it meets the baseline for a parameterless tool. It appropriately focuses on the tool's purpose rather than parameter details.

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: 'aggregate CI Visibility test events into buckets of computed metrics and timeseries.' It specifies the verb ('aggregate'), resource ('CI Visibility test events'), and output format ('buckets of computed metrics and timeseries'). However, it doesn't explicitly differentiate from sibling tools like 'aggregate_ci_pipelines_analytics' or 'aggregate_spans_analytics', which reduces clarity about when to choose this specific aggregation tool.

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 doesn't mention prerequisites, appropriate contexts, or compare it to sibling aggregation tools (e.g., 'aggregate_ci_pipelines_analytics' for pipeline data vs. test events). Without any usage context, an agent must infer based on the tool name alone.

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