Agentic MCP Client

A standalone agent runner that executes tasks using MCP (Model Context Protocol) tools via Anthropic Claude, AWS BedRock and OpenAI APIs. It enables AI agents to run autonomously in cloud environments and interact with various systems securely.

Current Features

• Included a basic agent dashboard
• Run standalone agents with tasks defined in JSON configuration files
• Support for both Anthropic Claude and OpenAI models
• Session logging for tracking agent progress

Run Dashboard Web

cd dashboard
npm i
npm run dev

Dashboard URL: http://localhost:3000
API Documentation: http://localhost:3000/api-docs

https://github.com/user-attachments/assets/c98be6d2-0096-40f2-bd78-d3fb256fec83

Installation

1. Clone the repository

2. Set up dependencies:

uv sync

3. Create an agent_worker_task.json file

Here is an example configuration file:

{
    "task": "Find all image files in the current directory and tell me their sizes",
    "model": "claude-3-7-sonnet-20250219",
    "system_prompt": "You are a helpful assistant that completes tasks using available tools.",
    "verbose": true,
    "max_iterations": 10
}

4. Run the agent:

uv run agentic_mcp_client/agent_worker/run.py

Configuration

The project requires a config.json file in the root directory to define the inference server settings and available MCP tools. Here's an example configuration:

{
   "inference_server": {
      "base_url": "https://api.anthropic.com/v1/",
      "api_key": "YOUR_API_KEY_HERE",
      "use_bedrock": true,
      "aws_region": "us-east-1",
      "aws_access_key_id": "YOUR_AWS_ACCESS_KEY",
      "aws_secret_access_key": "YOUR_AWS_SECRET_KEY"
   },
   "mcp_servers": {
    "mcp-remote-macos-use": {
      "command": "docker",
      "args": [
        "run",
        "-i",
        "-e",
        "MACOS_USERNAME=your_username",
        "-e",
        "MACOS_PASSWORD=your_password",
        "-e",
        "MACOS_HOST=your_host_ip",
        "--rm",
        "buryhuang/mcp-remote-macos-use:latest"
      ]
    },
    "mcp-my-apple-remembers": {
      "command": "docker",
      "args": [
        "run",
        "-i",
        "-e",
        "MACOS_USERNAME=your_username",
        "-e",
        "MACOS_PASSWORD=your_password",
        "-e",
        "MACOS_HOST=your_host_ip",
        "--rm",
        "buryhuang/mcp-my-apple-remembers:latest"
      ]
    }
  }
}

Configuration Sections

Inference Server

The inference_server section configures the connection to your language model provider:

• base_url: The API endpoint for your chosen LLM provider
• api_key: Your authentication key for the LLM service
• use_bedrock: Set to true to use Amazon Bedrock for model inference
• AWS credentials (when using Bedrock)

MCP Servers

The mcp_servers section defines available MCP tools. Each tool has:

• A unique identifier (e.g., "mcp-remote-macos-use")
• command: The command to execute (typically Docker for containerized tools)
• args: Configuration parameters for the tool

This example shows MCP tools for remotely controlling a macOS system through Docker containers.

How MCP Works

The Model Context Protocol provides a standardized way for applications to:

• Share contextual information with language models
• Expose tools and capabilities to AI systems
• Build composable integrations and workflows

The protocol uses JSON-RPC 2.0 messages to establish communication between hosts (LLM applications), clients (connectors within applications), and servers (services providing context and capabilities).

Our agent worker implements this workflow:

1. Initialize MCP clients for all available tools
2. Send the initial task message to the selected model
3. Process model responses (either tool calls or text)
4. If a tool call is

... View full README on GitHub