Cost Explorer MCP Server

Cost Explorer MCP Server

Enables querying and analyzing AWS cost and usage data through the AWS Cost Explorer API. Supports cost comparisons, forecasting, dimension filtering, and cost change driver analysis with streamable HTTP deployment to Amazon Bedrock Agentcore Runtime.

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README

Cost Explorer MCP Server - Agentcore Runtime Deployment

This directory contains the Cost Explorer MCP Server configured for deployment to Amazon Bedrock Agentcore Runtime with streamable HTTP support on port 8000.

๐Ÿ—๏ธ Architecture

  • Original: STDIO-based MCP server for local development
  • Converted: Streamable HTTP server for Agentcore Runtime deployment
  • Protocol: MCP (Model Context Protocol)
  • Transport: Streamable HTTP on port 8000
  • Deployment: Agentcore Runtime (no Agentcore memory)

๐Ÿ“ Project Structure

cost-explorer-agentcore-deployment/
โ”œโ”€โ”€ mcp_server.py              # Main MCP server with streamable HTTP
โ”œโ”€โ”€ requirements.txt           # Python dependencies
โ”œโ”€โ”€ __init__.py               # Python package marker
โ”œโ”€โ”€ awslabs/                  # Cost Explorer implementation
โ”‚   โ””โ”€โ”€ cost_explorer_mcp_server/
โ”œโ”€โ”€ deploy.py                 # Deployment automation script
โ”œโ”€โ”€ test_mcp_client.py        # Local testing client
โ”œโ”€โ”€ test_remote_client.py     # Remote testing client
โ””โ”€โ”€ README.md                 # This file

๐Ÿš€ Quick Start

1. Prerequisites

# Install required tools
pip install bedrock-agentcore-starter-toolkit
pip install mcp[cli]

# Configure AWS credentials
aws configure

2. Test Locally First

# Install dependencies
pip install -r requirements.txt

# Start the MCP server locally
python mcp_server.py

In another terminal:

# Test the local server
python test_mcp_client.py

3. Deploy to Agentcore Runtime

# Run the deployment script
python deploy.py

The script will:

  • Check prerequisites
  • Configure the deployment
  • Deploy to Agentcore Runtime
  • Save the Agent ARN to agent_arn.txt

4. Test Remote Deployment

# Set environment variables
export AGENT_ARN="your-agent-arn-from-deployment"
export BEARER_TOKEN="your-oauth-token"

# Test the deployed server
python test_remote_client.py

๐Ÿ”ง Manual Deployment Steps

If you prefer manual deployment:

1. Configure Deployment

agentcore configure -e mcp_server.py --protocol MCP

Follow the prompts:

  • Protocol: MCP
  • Entry point: mcp_server.py
  • Authentication: Choose OAuth or Cognito based on your needs

2. Deploy

agentcore launch

๐Ÿ” Authentication Setup

Option 1: Cognito User Pool (Recommended)

  1. Create a Cognito User Pool in AWS Console
  2. Configure the user pool for your application
  3. Use the provided credentials during deployment configuration

Option 2: OAuth with Auth0

  1. Set up Auth0 application with Dynamic Client Registration
  2. Configure OAuth settings during deployment
  3. Use Auth0 tokens for authentication

๐Ÿงช Testing

Local Testing

# Start server
python mcp_server.py

# Test in another terminal
python test_mcp_client.py

Remote Testing

# Set environment variables
export AGENT_ARN="arn:aws:bedrock-agentcore:us-west-2:123456789012:runtime/cost-explorer-xyz123"
export BEARER_TOKEN="your-oauth-token"

# Test deployed server
python test_remote_client.py

๐Ÿ› ๏ธ Available Tools

The Cost Explorer MCP Server provides these tools:

  1. get_today_date - Get current date for relative queries
  2. get_dimension_values - Get available values for dimensions (SERVICE, REGION, etc.)
  3. get_tag_values - Get available tag values
  4. get_cost_and_usage - Retrieve cost and usage data with filtering
  5. get_cost_and_usage_comparisons - Compare costs between periods
  6. get_cost_comparison_drivers - Analyze cost change drivers
  7. get_cost_forecast - Generate cost forecasts

๐Ÿ’ฐ Cost Considerations

Important: Each AWS Cost Explorer API call costs $0.01. The server includes optimizations to minimize API calls, but be aware of potential charges when using the tools extensively.

๐Ÿ” Troubleshooting

Local Server Issues

# Check if port 8000 is available
lsof -i :8000

# Check server logs
python mcp_server.py

Deployment Issues

# Check AWS credentials
aws sts get-caller-identity

# Verify agentcore CLI
agentcore --version

# Check deployment status
agentcore status

Remote Connection Issues

  1. Verify BEARER_TOKEN is valid and not expired
  2. Check AGENT_ARN format and encoding
  3. Ensure OAuth/Cognito is properly configured
  4. Verify server deployment status

๐Ÿ“š References

๐Ÿ†˜ Support

For issues with:

  • MCP Server: Check the original cost-explorer-mcp-server documentation
  • Agentcore Runtime: Refer to AWS Bedrock Agentcore documentation
  • Authentication: Check your OAuth/Cognito configuration
  • AWS Costs: Review AWS Cost Explorer API pricing and usage

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