S

Sample Cloud Spend MCP Server

A tool for querying AWS cloud spending through the MCP protocol, integrating the Claude model to provide a natural language interaction interface.
2.5 points
15

What is the AWS Cost Analysis MCP Server?

This is a server based on the Anthropic MCP protocol that allows you to query AWS cost data through natural language. It can help you better understand the costs of EC2, Bedrock, and other AWS services.

How to use the AWS Cost Analysis MCP Server?

You can set it up and start querying your AWS cost data in just a few steps. You can use a local or remote server to run the tool.

Applicable Scenarios

Suitable for enterprises and individuals who need to regularly monitor and analyze AWS costs, especially those who want to optimize cloud spending.

Main Features

EC2 Cost AnalysisView the specific cost situation of EC2 within the most recent day.
Bedrock Usage StatisticsAnalyze the daily usage of Bedrock models by region and user.
Service Overview ReportGenerate a summary report of all AWS services in the past 30 days.

Advantages and Limitations

Advantages
Supports multiple query methods (natural language)
Provides detailed cost breakdown
Easy to integrate into existing systems
Limitations
Requires valid AWS credentials
May not be suitable for very large account sizes

How to Use

Install Dependencies
Ensure that Python 3.12 and the necessary libraries are installed.
Configure Environment Variables
Set necessary parameters such as AWS access keys and regions.
Start the Server
Run the script to start the MCP server.

Usage Examples

Query the Total Bill of Last MonthAsk the server for the total AWS bill of last month.
Analyze EC2 SpendingAnalyze the EC2 spending within the most recent day.

Frequently Asked Questions

What permissions do I need to use this tool?
How to ensure data security?

Related Resources

Official Documentation
AWS official documentation.
GitHub Repository
Project source code repository.
Installation
Copy the following command to your Client for configuration
{
  "mcpServers": {
    "aws-cost-explorer": {
      "command": "docker",
      "args": [ "run", "-i", "--rm", "-e", "AWS_ACCESS_KEY_ID", "-e", "AWS_SECRET_ACCESS_KEY", "-e", "AWS_REGION", "-e", "BEDROCK_LOG_GROUP_NAME", "-e", "MCP_TRANSPORT", "aws-cost-explorer-mcp:latest" ],
      "env": {
        "AWS_ACCESS_KEY_ID": "YOUR_ACCESS_KEY_ID",
        "AWS_SECRET_ACCESS_KEY": "YOUR_SECRET_ACCESS_KEY",
        "AWS_REGION": "us-east-1",
        "BEDROCK_LOG_GROUP_NAME": "YOUR_CLOUDWATCH_BEDROCK_MODEL_INVOCATION_LOG_GROUP_NAME",
        "MCP_TRANSPORT": "stdio"
      }
    }
  }
}

{
  "mcpServers": {
    "aws_cost_explorer": {
      "command": "uv",
      "args": [
          "--directory",
          "/path/to/aws-cost-explorer-mcp-server",
          "run",
          "server.py"
      ],
      "env": {
        "AWS_ACCESS_KEY_ID": "YOUR_ACCESS_KEY_ID",
        "AWS_SECRET_ACCESS_KEY": "YOUR_SECRET_ACCESS_KEY",
        "AWS_REGION": "us-east-1",
        "BEDROCK_LOG_GROUP_NAME": "YOUR_CLOUDWATCH_BEDROCK_MODEL_INVOCATION_LOG_GROUP_NAME",
        "MCP_TRANSPORT": "stdio"
      }
    }
  }
}
Note: Your key is sensitive information, do not share it with anyone.
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