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 indicates this is a read operation ('Returns'), but doesn't address important behavioral aspects: whether this requires specific permissions, what the response format looks like, whether there are rate limits, whether it's a real-time or cached estimate, or what happens if the metric doesn't exist. The description mentions the technology ('Metrics without Limits™') but doesn't explain its implications for the tool's behavior.

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, reasonably concise sentence that front-loads the core purpose. It efficiently communicates the tool's function without unnecessary words. The trademark symbol adds minor clutter but doesn't significantly impact readability. Every part of the sentence contributes to understanding the tool's purpose.

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 (100% schema coverage) but no output schema and no annotations, the description provides adequate basic purpose information. However, for a tool that returns estimated cardinality - which could involve complex calculations and specific constraints - the description should ideally address more behavioral context, especially since there's no output schema to indicate what the return value looks like. The mention of 'Metrics without Limits™' provides some technology context but doesn't fully compensate for the lack of behavioral details.

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% schema description coverage, so the baseline is 4. The description adds value by specifying what configuration elements are needed ('a given tag, percentile and number of aggregations configuration'), which provides semantic context beyond the empty schema. However, it doesn't detail the format or constraints of these configuration elements.

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: 'Returns the estimated cardinality for a metric with a given tag, percentile and number of aggregations configuration using Metrics without Limits™.' It specifies the verb ('Returns'), resource ('estimated cardinality for a metric'), and key configuration elements (tag, percentile, aggregations). However, it doesn't explicitly distinguish this tool from its many sibling tools, which include various analytics and metric-related operations.

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. While it mentions 'Metrics without Limits™' as the underlying technology, it doesn't specify when this estimation approach is appropriate compared to other metric query tools in the sibling list (like 'metrics_query_scalars', 'metrics_query_timeseries', or 'get_metric_tag_cardinalities'). There are no usage prerequisites, exclusions, or alternative recommendations.

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