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MCP Datadog Server

MCP Datadog Server

by ClaudioLazaro
OverviewSchemaSourceRelated ServersDiscussionsAdmin
JavaScript
Remote

get_integration_aws

List all available Datadog-AWS integrations in your organization to monitor cloud infrastructure and services.

Instructions

List all Datadog-AWS integrations available in your Datadog organization.

Tool call template

This tool declares no required parameters. Add optional parameters from the input schema as needed.

{
  "id": 1,
  "jsonrpc": "2.0",
  "method": "tools/call",
  "params": {
    "arguments": {},
    "name": "get_integration_aws"
  }
}

Input Schema

NameRequiredDescriptionDefault

No arguments

Schema Changelog

Changes observed during successful MCP inspections.

  1. First observedv1.0.0

TDQS

B3.1/5.0
Behavior2/5

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. It only states what the tool does ('List all...') without mentioning any behavioral traits such as whether it's read-only, requires authentication, has rate limits, returns paginated results, or what the output format looks like.

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

Conciseness5/5

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

The description is a single, efficient sentence that directly states the tool's purpose without any unnecessary words. It's front-loaded and wastes no space, making it easy to understand quickly.

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

Completeness2/5

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

Given that there are no annotations and no output schema, the description is incomplete. It doesn't provide enough context for an agent to understand the tool's behavior, such as what the returned list contains, any limitations, or how to handle the output. For a tool with no structured metadata, more detail is needed.

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, and schema description coverage is 100%, so there's no need for parameter details in the description. The description appropriately doesn't discuss parameters, which is sufficient for a parameterless tool.

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: 'List all Datadog-AWS integrations available in your Datadog organization.' It uses a specific verb ('List') and identifies the resource ('Datadog-AWS integrations'), though it doesn't explicitly differentiate from sibling tools like 'get_integration_aws_event_bridges' or 'get_integration_aws_logs'.

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 doesn't mention any prerequisites, context for usage, or comparisons with sibling tools that might fetch specific types of AWS integrations or related data.

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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