Skip to main content
Glama
brukhabtu

Datadog MCP Server

by brukhabtu

ListActiveMetricConfigurations

Retrieve active tags and aggregations queried on dashboards, notebooks, monitors, and Metrics Explorer for a specific metric, enabling efficient metric analysis and optimization.

Instructions

List tags and aggregations that are actively queried on dashboards, notebooks, monitors, the Metrics Explorer, and using the API for a given metric name.

Path Parameters:

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

Query Parameters:

  • window[seconds]: The number of seconds of look back (from now). Default value is 604,800 (1 week), minimum value is 7200 (2 hours), maximum value is 2,630,000 (1 month).

Responses:

  • 200 (Success): Success

    • Content-Type: application/json

    • Response Properties:

    • Example:

{
  "data": "unknown_type"
}
  • 400: Bad Request

    • Content-Type: application/json

    • Response Properties:

      • errors: A list of errors.

    • Example:

{
  "errors": [
    "Bad Request"
  ]
}
  • 403: Forbidden

    • Content-Type: application/json

    • Response Properties:

      • errors: A list of errors.

    • Example:

{
  "errors": [
    "Bad Request"
  ]
}
  • 404: Not Found

    • 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.
window[seconds]NoThe number of seconds of look back (from now). Default value is 604,800 (1 week), minimum value is 7200 (2 hours), maximum value is 2,630,000 (1 month).

Output Schema

TableJSON Schema
NameRequiredDescriptionDefault
dataNo
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 mentions the tool lists active configurations but does not disclose behavioral traits such as permissions required, rate limits, pagination, or whether it's a read-only operation. The description includes HTTP response codes (e.g., 429 for rate limiting), which adds some context, but lacks explicit behavioral guidance.

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

Conciseness3/5

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

The description is front-loaded with a clear purpose statement, but includes extensive, redundant details on HTTP responses and examples that are better suited for an output schema. This adds unnecessary length without enhancing tool understanding for an AI agent, reducing overall efficiency.

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 moderate complexity, 100% schema coverage, and the presence of an output schema, the description is mostly complete. It covers the purpose and parameters adequately but lacks behavioral context (e.g., auth needs, rate limits) and usage guidelines, which are important for a tool with no annotations.

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?

Schema description coverage is 100%, so the schema fully documents both parameters. The description adds minimal value beyond the schema by restating the metric_name parameter and providing context for window[seconds] in the query parameters section. However, it does not explain parameter interactions or provide additional semantic insights, warranting a baseline-adjusted score.

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

Purpose5/5

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

The description clearly states the specific action ('List tags and aggregations that are actively queried') and resource ('for a given metric name'), with explicit scope ('on dashboards, notebooks, monitors, the Metrics Explorer, and using the API'). It distinguishes from sibling tools by focusing on active metric configurations rather than general listings or other operations.

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

Usage Guidelines3/5

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

The description implies usage when needing to see active configurations for a specific metric, but does not explicitly state when to use this tool versus alternatives like 'ListTagConfigurations' or 'ListTagsByMetricName'. No exclusions or prerequisites are mentioned, leaving some ambiguity about optimal use cases.

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

Install Server

Other Tools

Related Tools

Latest Blog Posts

MCP directory API

We provide all the information about MCP servers via our MCP API.

curl -X GET 'https://glama.ai/api/mcp/v1/servers/brukhabtu/datadog-mcp'

If you have feedback or need assistance with the MCP directory API, please join our Discord server