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brukhabtu

Datadog MCP Server

by brukhabtu

ListMetricAssets

Identify dashboards, monitors, notebooks, and SLOs associated with a specific metric in Datadog. Updated daily to ensure accurate insights into metric usage.

Instructions

Returns dashboards, monitors, notebooks, and SLOs that a metric is stored in, if any. Updated every 24 hours.

Path Parameters:

  • metric_name (Required): The name of the metric.

Responses:

  • 200 (Success): Success

    • Content-Type: application/json

    • Response Properties:

      • included: Array of objects related to the metric assets.

    • Example:

{
  "data": "unknown_type",
  "included": [
    "unknown_type"
  ]
}
  • 400: API error response.

    • Content-Type: application/json

    • Response Properties:

      • errors: A list of errors.

    • Example:

{
  "errors": [
    "Bad Request"
  ]
}
  • 403: API error response.

    • Content-Type: application/json

    • Response Properties:

      • errors: A list of errors.

    • Example:

{
  "errors": [
    "Bad Request"
  ]
}
  • 404: API error response.

    • Content-Type: application/json

    • Response Properties:

      • errors: A list of errors.

    • Example:

{
  "errors": [
    "Bad Request"
  ]
}
  • 429: Too Many Requests

    • Content-Type: application/json

    • Response Properties:

      • errors: A list of errors.

    • Example:

{
  "errors": [
    "Bad Request"
  ]
}

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
metric_nameYesThe name of the metric.

Output Schema

TableJSON Schema
NameRequiredDescriptionDefault
dataNo
includedNoArray of objects related to the metric assets.
Behavior3/5

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

No annotations are provided, so the description carries the full burden. It discloses that data is 'Updated every 24 hours,' indicating a caching behavior, and includes error responses (e.g., 429 for rate limits), which adds useful context. However, it lacks details on permissions, side effects, or response structure beyond basic HTTP codes, leaving gaps in behavioral understanding.

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?

The description is overly verbose and poorly structured, including extensive HTTP response details that clutter the core purpose. It mixes high-level intent with low-level API documentation, making it less front-loaded and efficient. Sentences like 'Updated every 24 hours' are useful, but the bulk of text does not earn its place for tool selection.

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

Completeness4/5

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

Given the tool's low complexity (1 parameter) and high schema coverage (100%), the description is mostly complete. It includes behavioral notes (e.g., update frequency) and error handling, and an output schema exists, so return values need not be explained. However, the lack of usage guidelines and cluttered structure slightly reduces completeness.

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

Parameters3/5

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

The input schema has 100% description coverage, with 'metric_name' clearly documented. The description repeats this in a 'Path Parameters' section but does not add meaningful semantics beyond the schema, such as format examples or constraints. Since the schema does the heavy lifting, the baseline score of 3 is appropriate.

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

Purpose4/5

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 dashboards, monitors, notebooks, and SLOs that a metric is stored in, if any.' It specifies the verb ('returns') and the resource types (dashboards, monitors, notebooks, SLOs) with a metric as input. However, it does not explicitly differentiate from sibling tools like 'ListTagsByMetricName' or 'ListVolumesByMetricName', which also list metric-related items, leaving some ambiguity.

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

Usage Guidelines2/5

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 mentions that data is 'Updated every 24 hours,' which implies a caching or freshness constraint, but does not specify use cases, prerequisites, or comparisons to sibling tools like 'ListTagsByMetricName' or 'ListVolumesByMetricName' for metric-related queries.

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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