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 aggregation into 'buckets of computed metrics and timeseries,' which hints at read-only data processing, but doesn't specify whether this is a query, report generation, or analysis tool. Critical details like authentication requirements, rate limits, or whether it modifies data are absent, leaving significant gaps for an agent.

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 the tool's function without unnecessary words. It's front-loaded with the core action ('aggregate') and resource, making it easy to parse. However, it could be slightly more structured by explicitly mentioning the lack of parameters or output format.

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 what the tool does (aggregation into metrics/timeseries). However, without annotations, it lacks behavioral context (e.g., read-only vs. mutative, performance implications). For a tool with no parameters, the description is minimally complete but misses opportunities to clarify usage and behavioral traits.

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 no parameters are documented because none exist. The description doesn't need to add parameter semantics, as there are no parameters to explain. A baseline of 4 is appropriate since the schema fully covers the lack of parameters.

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 pipeline events into buckets of computed metrics and timeseries.' It specifies the verb ('aggregate'), resource ('CI Visibility pipeline events'), and output format ('buckets of computed metrics and timeseries'), making the function unambiguous. However, it doesn't explicitly differentiate from sibling tools like 'aggregate_ci_tests_analytics' or 'aggregate_spans_analytics', which likely aggregate different data 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?

The description provides no guidance on when to use this tool versus alternatives. It doesn't mention any prerequisites, context for usage, or comparisons with sibling tools (e.g., 'aggregate_ci_tests_analytics' for test events). The phrase 'Use this API endpoint' is generic and doesn't offer actionable usage instructions.

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