What is Q 2001

Q-2001 is a command-line AI assistant powered by AWS Bedrock, designed to answer questions, provide help, and engage in conversations using advanced large language models with intelligent tool capabilities through Model Context Protocol (MCP) integration.

Use cases

Use cases for Q-2001 include automating customer queries, providing coding assistance, facilitating educational tutoring, and enhancing productivity through intelligent tool integration.

How to use

To use Q-2001, ensure Python 3.11+ is installed, install the required dependencies, configure your AWS credentials, and run the command line interface with various options to specify models, profiles, and regions.

Key features

Key features of Q-2001 include a simple command line interface, support for multiple AWS Bedrock models, real-time streaming responses, conversation history management, tool usage capabilities through MCP integration, and customizable configurations.

Where to use

Q-2001 can be used in various fields such as software development, customer support, education, and any domain requiring interactive AI assistance.

Agent CLI MCP on AWS Bedrock (AKA Q-2001)

Q-2001 is a command-line AI assistant powered by AWS Bedrock. It uses advanced large language models to answer questions, provide help, and engage in conversations, with intelligent tool capabilities through Model Context Protocol (MCP) integration.

Features

• Simple and easy-to-use command line interface with rich formatting
• Support for multiple AWS Bedrock models (Claude, DeepSeek, Nova, etc.)
• Real-time streaming responses
• Conversation history and context management
• Tool usage capabilities through MCP integration
• Customizable configuration

Installation

1. Ensure Python 3.11+ is installed
2. Install dependencies:

pip install -r requirements.txt

3. Ensure AWS credentials are configured (via AWS CLI or environment variables)

Usage

# Basic usage
python Q-2001.py

# Specify model
python Q-2001.py --model us.anthropic.claude-3-7-sonnet-20250219-v1:0

# Use other models
python Q-2001.py --model us.deepseek.r1-v1:0
python Q-2001.py --model us.amazon.nova-micro-v1:0

# Specify AWS profile
python Q-2001.py --profile myprofile

# Specify AWS region
python Q-2001.py --region us-east-1

# Show version
python Q-2001.py --version

Commands

The following commands are available in Q-2001:

• /quit or /exit: Exit the application
• /help: Display help information
• /clear: Clear conversation history
• /model <model_id>: Change AI model
• /tools: List all available MCP tools

MCP Integration

Q-2001 supports extended functionality through Model Context Protocol (MCP). The MCP client handles the integration with various tool servers.

Configuration

Q-2001 creates a configuration file at ~/.q2001_mcp_config.json which can be manually edited to change default settings.

Example configuration:

{
  "mcpServers": {
    "aws-document": {
      "command": "uvx",
      "args": [
        "awslabs.aws-documentation-mcp-server@latest"
      ]
    },
    "mcp-server-time": {
      "command": "uvx",
      "args": [
        "mcp-server-time",
        "--local-timezone=America/New_York"
      ]
    },
    "filesystem": {
      "command": "npx",
      "args": [
        "-y",
        "@modelcontextprotocol/server-filesystem",
        "/home/ec2-user/"
      ]
    },
    "sequential-thinking": {
      "command": "npx",
      "args": [
        "-y",
        "@modelcontextprotocol/server-sequential-thinking"
      ]
    }
  }
}

Tool Capabilities

When MCP servers are configured and initialized, Q-2001 can:

1. Automatically detect when tools are needed to solve a problem
2. Call appropriate tools with proper parameters
3. Process tool results and incorporate them into responses
4. Handle multiple tool calls in a single conversation

Requirements

• Python 3.11+
• Required Python packages:
  • boto3
  • rich
  • mcp
  • anyio
  • markdown-it-py
  • uvicorn
  • python-dotenv
  • httpx
  • pygments
• AWS account and appropriate permissions to access Bedrock services
• Configured AWS credentials
• Node.js and npm (for npx commands with certain MCP servers)

Notes

• Using this tool will incur AWS Bedrock API call costs
• Ensure your AWS account has Bedrock service enabled and access to required models