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ClaudioLazaro

MCP Datadog Server

metrics_query_scalars

Query scalar metrics from Datadog to monitor system performance and track key numerical data points for analysis and alerting.

Instructions

Metrics: query scalars

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault

No arguments

Behavior1/5

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. The description 'Metrics: query scalars' reveals nothing about whether this is a read or write operation, what permissions might be required, whether it has side effects, rate limits, or what the response format looks like. For a query tool with zero annotation coverage, this represents a complete failure to disclose behavioral characteristics.

Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.

Conciseness2/5

Is the description appropriately sized, front-loaded, and free of redundancy?

While technically concise with just three words, the description is under-specified rather than efficiently structured. It lacks any meaningful front-loading of information - the tool name already contains 'metrics_query_scalars', so 'Metrics: query scalars' adds minimal value. Every word should earn its place, but here the words don't provide sufficient utility to justify even this brief description.

Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.

Completeness1/5

Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?

Given the complexity of a metrics query tool in a system with numerous sibling tools, the description is completely inadequate. With no annotations, no output schema, and a sparse description that doesn't explain what 'scalars' means or how this differs from other query tools, an AI agent would have insufficient information to understand when and how to use this tool correctly. The description fails to provide the necessary context for effective tool selection.

Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.

Parameters4/5

Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?

The tool has 0 parameters with 100% schema description coverage (empty schema). Since there are no parameters to document, the description doesn't need to compensate for any gaps. The baseline for zero parameters is 4, as there's no parameter information that could be missing or inadequately described.

Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.

Purpose2/5

Does the description clearly state what the tool does and how it differs from similar tools?

The description 'Metrics: query scalars' is a tautology that essentially restates the tool name with minimal additional information. It indicates the domain (metrics) and action (query scalars) but lacks specificity about what 'scalars' means in this context or what resources are involved. Compared to siblings like 'metrics_query_timeseries' or 'query_timeseries', it doesn't clearly differentiate what makes this tool unique.

Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.

Usage Guidelines1/5

Does the description explain when to use this tool, when not to, or what alternatives exist?

The description provides absolutely no guidance on when to use this tool versus alternatives. With siblings like 'metrics_query_timeseries', 'query_timeseries', 'query_scalars', and various aggregate analytics tools, there's no indication of what scenarios warrant using this specific metrics query tool. The description offers no context about appropriate use cases or prerequisites.

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

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