AWS Bedrock Logs MCP: AI-Driven Insights & Log Optimization

What is AWS Bedrock Logs MCP: AI-Driven Insights & Log Optimization?

AWS Bedrock Logs MCP is a tool that leverages artificial intelligence to analyze and interpret logs generated by AWS Bedrock, Amazon's machine learning service. By integrating with Anthropic's Model Control Protocol (MCP) and the Claude AI model, this solution provides actionable insights into resource utilization, cost optimization, and operational efficiency. It automates the process of parsing log data, identifying patterns, and delivering real-time recommendations to streamline Bedrock deployments.

How to Use AWS Bedrock Logs MCP: AI-Driven Insights & Log Optimization?

1. Install Dependencies: Begin by setting up the uv command-line tool and cloning the project repository via git clone.
2. Configure Environment: Create a Python virtual environment, install dependencies, and configure AWS credentials with proper IAM permissions.
3. Launch the Service: Execute the MCP server using predefined commands and integrate with the Claude API for analysis.
4. Query & Analyze: Submit natural language queries to the system through CLI or API endpoints to retrieve insights on specific log patterns or anomalies.

Key Features of AWS Bedrock Logs MCP: AI-Driven Insights & Log Optimization?

• Model Utilization Analysis: Visualize training/ inference workload distribution across Bedrock models in real time.
• User-Specific Consumption Tracking: Segment cost and resource usage by team, project, or individual user accounts.
• Proactive Anomaly Detection: Automatically flag unusual API call patterns or unexpected latency spikes through AI-powered monitoring.
• Compliance Reporting: Generate audit-ready reports on data retention policies and regulatory adherence.

Use Cases of AWS Bedrock Logs MCP: AI-Driven Insights & Log Optimization?

Organizations use this solution for:

• Optimizing cloud spend by identifying underutilized models or excessive API calls
• Enforcing security policies through automated anomaly detection in sensitive operations
• Accelerating incident response by correlating log events with service outages
• Streamlining DevOps workflows with CI/CD pipeline integrations for log analysis

FAQ from AWS Bedrock Logs MCP: AI-Driven Insights & Log Optimization?

• Q: Does this require specific Python versions?
  A: Supported on Python 3.8+ as specified in the requirements.txt.
• Q: How is data security ensured?
  A: All log processing occurs within customer VPCs using encrypted channels and role-based access controls.
• Q: Can it scale for large enterprises?
  A: Built with distributed architecture to handle petabyte-scale log volumes through AWS Auto Scaling groups.
• Q: What's the latency for query responses?
  A: Typical sub-second response times for pre-indexed data using vector database optimizations.

AWS Bedrock Logs MCP

A command-line interface and API for analyzing AWS Bedrock usage and logs through Anthropic's MCP (Model Control Protocol).

Overview

This tool provides a convenient way to analyze AWS Bedrock model invocation logs using Anthropic's Claude model as an interactive interface. It functions as an MCP server that exposes AWS CloudWatch Logs API functionality to Claude, allowing you to query and analyze your Bedrock usage data in natural language.

Features

• Model Usage Analysis : View detailed statistics about Bedrock model usage and token consumption
• User-based Analytics : Analyze usage patterns and costs by user
• Daily Usage Reports : Track daily usage trends and model invocations
• Token Consumption Metrics : Monitor input, completion, and total token usage
• Interactive Interface : Use Claude to query your Bedrock usage data through natural language

Requirements

• Python 3.13+
• AWS credentials with CloudWatch Logs access
• Anthropic API access (for Claude integration)

Installation

1. Install uv:

   On macOS and Linux

curl -LsSf https://astral.sh/uv/install.sh | sh


    # On Windows
powershell -ExecutionPolicy ByPass -c "irm https://astral.sh/uv/install.ps1 | iex"

2. Clone this repository:

   git clone https://github.com/dheerajoruganty/aws-bedrock-logs-mcp-server.git

cd aws-bedrock-logs-mcp-server

3. Set up the Python virtual environment and install dependencies:

   uv venv && source .venv/bin/activate && uv pip sync pyproject.toml

For Windows:

    uv venv && .venv\Scripts\activate && uv pip sync pyproject.toml

4. Configure your AWS credentials:

   mkdir -p ~/.aws

# Set up your credentials in ~/.aws/credentials and ~/.aws/config

Usage

Starting the Server

Run the server using:

python cloudwatch_mcp_server.py

By default, the server uses stdio transport for communication with MCP clients.

Claude Desktop Configuration

Configure this tool with Claude Desktop:

{
  "mcpServers": {
    "aws_bedrock_logs": {
      "command": "uv",
      "args": [
          "--directory",
          "/path/to/aws-bedrock-logs-mcp",
          "run",
          "cloudwatch_mcp_server.py"
      ]
    }
  }
}

Make sure to replace the directory path with the actual path to your repository on your system.

Available Tools

The server exposes the following tools that Claude can use:

1. get_bedrock_logs_df : Retrieve raw Bedrock invocation logs as a pandas DataFrame
2. get_model_usage_stats : Get usage statistics grouped by model
3. get_user_usage_stats : Get usage statistics grouped by user
4. get_daily_usage_stats : Get daily usage statistics and trends

Example Queries

Once connected to Claude through an MCP-enabled interface, you can ask questions like:

• "Show me the Bedrock usage stats for the last 7 days"
• "What's the average token consumption by model?"
• "Who are the top users of Bedrock in terms of total tokens?"
• "Give me a daily breakdown of model invocations"

Development

Project Structure

• cloudwatch_mcp_server.py: Main server implementation with MCP tools
• pyproject.toml: Project dependencies and metadata
• Dockerfile: Container definition for deployments

Dependencies

Key dependencies include:

• boto3: AWS SDK for Python
• mcp[cli]: Anthropic's Model Control Protocol
• pandas: Data manipulation and analysis
• pydantic: Data validation using Python type annotations

License

MIT License

Acknowledgments

• This tool uses Anthropic's MCP framework
• Powered by AWS CloudWatch Logs API
• Built with FastMCP for server implementation