What is AWS EC2 Pricing MCP Server?

AWS EC2 Pricing MCP Server is a tool that provides fast, real-time EC2 pricing data via a pre-parsed AWS pricing catalogue, allowing automation scripts and LLMs to query AWS instance pricing with a single call. It helps answer questions like finding the cheapest instance with specific RAM, looking up CPU speeds for AMD instances, checking discounts for reserved instances, or comparing costs for Windows with SQL Server Enterprise instances.

How to use AWS EC2 Pricing MCP Server?

Using Docker

1. Docker Hub Image: Run a pre-built image by providing a mcp_config.json with the command to run the container.
2. Local Image: Build the image locally by downloading the pricing data, then configure it similarly to the Docker Hub version.

Using Python Directly

1. Download the latest pricing data from AWS S3.
2. Place it alongside server.py and configure a mcp_config.json file to run the script.

Key Features of AWS EC2 Pricing MCP Server?

• Real-time pricing queries: Instantly retrieve EC2 instance pricing data.
• Pre-parsed catalogue: Optimized for fast performance.
• Flexible querying: Supports complex filters (e.g., RAM, CPU speed, discounts).
• Secure execution: Docker runs in isolated mode (--network none) for privacy.

Use Cases of AWS EC2 Pricing MCP Server?

1. Cost analysis: Compare expenses across instance types or regions.
2. Automation: Integrate pricing checks into CI/CD or deployment scripts.
3. Benchmarking: Find the most cost-effective instance for workloads.
4. Research: Analyze trends like reserved instance discounts over time.

FAQ from AWS EC2 Pricing MCP Server?

• How often is the pricing data updated?

  The data is refreshed based on AWS catalogue updates. Rebuilding the image with BUILD_DATE ensures fresh data.

• Can this be deployed on-premise?

  Yes, both Docker and local Python modes support private deployment.

• Does this track historical pricing?

  No, it only includes current pricing. For history, download past catalogues manually.

AWS EC2 Pricing MCP Server

The AWS EC2 Pricing MCP Server lets any LLM or automation script query real-time EC2 pricing with one call. Powered by a pre-parsed AWS pricing catalogue, it answers questions such as

What is the cheapest EC2 instance with 32GB RAM?

Which AMD instances have more than 3.5 Ghz CPUs?

What is the 3-yr All Upfront discount on r6g family in eu-west-1?

What is the cheapest instance to run Windows with SQL Server Enterprise?

Using Docker

Using a Docker hub image

Use this mcp_config.json for Docker hub image:

{
  "mcpServers": {
    "AWS EC2 Pricing MCP": {
      "command": "docker",
      "args": ["run", "--rm", "-i", "-q", "--network", "none", "ai1st/aws-pricing-mcp"]
    }
  }
}

• The --rm flag removes the container when it exits.
• The -i flag enables interactive mode for stdio communication.
• The -q flag suppresses the docker messages about downloading the image.
• The --network none totally disconnects the container from the network to guarantee no data exfiltration.

Using a local image

Build the image:

docker build -t aws-pricing-mcp . --build-arg BUILD_DATE=$(date +%Y-%m-%d)

This will download the pricing data and build the image. The BUILD_DATE parameter ensures the fresh pricing data is downloaded during build.

Sample mcp_config.json for a locally built image:

{
  "mcpServers": { "AWS EC2 Pricing MCP": {
      "command": "docker",
      "args": ["run", "--rm", "-i", "--network", "none", "aws-pricing-mcp"]
    }
  }
}

• The --rm flag removes the container when it exits.
• The -i flag enables interactive mode for stdio communication.
• The --network none totally disconnects the container from the network to guarantee no data exfiltration.

Using Python directly

You'll need to download the pricing data first:

curl https://cloudfix-public-aws-pricing.s3.us-east-1.amazonaws.com/pricing/ec2_pricing.json.gz | gunzip > ec2_pricing.json

It should be in the same directory as server.py.

Sample mcp_config.json for local Python:

{
  "mcpServers": { "AWS EC2 Pricing MCP": {
      "command": "python",
      "args": [
        "/path/to/server.py"
      ]
    }
  }
}

Building Instructions

For instructions on building and publishing the Docker image, see BUILD.md.

Pricing Data JSON Format

See PRICING.md.