Skip to main content
Glama
brukhabtu

Datadog MCP Server

by brukhabtu

ConvertExistingSecurityMonitoringRule

Convert Datadog security monitoring rules from JSON to Terraform format for the datadog provider resource datadog_security_monitoring_rule. Simplify rule management in Terraform configurations.

Instructions

Convert an existing rule from JSON to Terraform for datadog provider resource datadog_security_monitoring_rule.

Path Parameters:

  • rule_id (Required): The ID of the rule.

Responses:

  • 200 (Success): OK

    • Content-Type: application/json

    • Response Properties:

      • terraformContent: Terraform string as a result of converting the rule from JSON.

    • Example:

{
  "terraformContent": "string"
}
  • 400: Bad Request

    • Content-Type: application/json

    • Response Properties:

      • errors: A list of errors.

    • Example:

{
  "errors": [
    "Bad Request"
  ]
}
  • 403: Not Authorized

    • 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
rule_idYesThe ID of the rule.

Output Schema

TableJSON Schema
NameRequiredDescriptionDefault
terraformContentNoTerraform string as a result of converting the rule from JSON.
Behavior2/5

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 mentions the conversion action but lacks details on permissions required, rate limits (implied by the 429 response but not explicitly stated), side effects, or error handling beyond HTTP codes. This leaves significant gaps for a tool that likely requires authentication and has operational constraints.

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, with extensive HTTP response details that clutter the core purpose. It includes redundant parameter information and multiple error examples, making it less efficient and harder to parse quickly for an AI agent.

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

Completeness3/5

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

Given the tool's complexity (conversion operation), lack of annotations, and presence of an output schema, the description is partially complete. It covers the basic purpose and parameters but misses key behavioral aspects like authentication needs or rate limits, which are crucial for safe invocation.

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 schema description coverage is 100%, with the single parameter 'rule_id' fully documented in the schema. The description repeats this information in the 'Path Parameters' section but adds no additional meaning, such as format examples or sourcing instructions, resulting in a baseline 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 ('Convert an existing rule from JSON to Terraform') and identifies the target resource ('datadog_security_monitoring_rule'). It distinguishes itself from sibling tools like 'GetSecurityMonitoringRule' by focusing on conversion rather than retrieval, making the purpose explicit and differentiated.

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 does not mention prerequisites (e.g., needing an existing rule ID), exclusions, or comparisons to other tools, leaving the agent without context for appropriate selection.

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