mcp-server-agent-comm
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A sophisticated multi-agent communication framework that enables seamless collaboration between AI agents through MCP (Model Context Protocol) tools.
Agent Communication System
A sophisticated multi-agent communication framework that enables seamless collaboration between AI agents through MCP (Model Context Protocol) tools, with support for real-time message routing, admin control, and dual-language operation.
š Features
- Multi-Agent Communication: Enable Agent 1 and Agent 2 to communicate efficiently
- Admin Control System: Absolute priority commands with SOURCE tag authority
- Real-time Message Routing: Smart delivery and manual routing options
- Dual Language Support: Vietnamese and English rule sets
- Advanced UI Controller: Comprehensive interface for message management
- File & Image Attachments: Support for mixed content communication
- Workspace-Aware: Intelligent path processing for different workspaces
š Prerequisites
- Python
- MCP-compatible AI environment (e.g., Claude, Cursor)
- Git for repository cloning
āļø Installation
1. Clone Repository
git clone https://github.com/your-repo/mcp-server-agent-comm.git
cd mcp-server-agent-comm
2. Install Dependencies
pip install -r requirements.txt
3. MCP Server Configuration
Add the following configuration to your MCP settings:
{
"agent_chat_1": {
"command": "python",
"args": ["E:/MCP-servers-github/Utils/mcp_server_agent1.py"],
"stdio": true,
"enabled": true
},
"agent_chat_2": {
"command": "python",
"args": ["E:/MCP-servers-github/Utils/mcp_server_agent2.py"],
"stdio": true,
"enabled": true
}
}
Note: Update the path E:/MCP-servers-github/Utils/
to match your actual installation directory.
š Rule Configuration
Language Options
Choose one of the rule files based on your preferred language:
- Vietnamese:
rule_for_AI_VI.txt
- English:
rule_for_AI_EN.txt
Setup in Cursor
- Open Cursor settings
- Navigate to "Rules for AI" section
- Copy and paste the content of your chosen rule file
- Save the configuration
š Usage
Step 1: Start Controller UI
Open your terminal/command prompt and run:
python E:\MCP-servers-github\Utils\main_controller.py
Note: Replace E:\MCP-servers-github\Utils\
with your actual installation path.
The Controller UI will open, allowing you to monitor and control agent communication.
Feature "AI Chat" -> user can chat with all waiting agent.
Step 2: Setup Agents in Cursor
- Open Two Tabs: Create two separate chat tabs in Cursor
- Tab 1 - Agent 1: Type activation command to start Agent 1
- Tab 2 - Agent 2: Type activation command to start Agent 2
- Execute Tools: Allow AI to call the MCP server agent chat tools
- Monitor Controller: Check Controller UI for registered agents
- Route Messages: Use Controller UI to manage message delivery
Activation Commands
For AI Interaction Mode:
- Vietnamese:
start ai_interaction
- English:
start ai_interaction
For Agent Communication Mode:
- Vietnamese:
start agent chat 1
orstart agent chat 2
- English:
start agent chat 1
orstart agent chat 2
Communication Flow
- Agent Registration: Agents register with their respective tools
- Message Routing: Controller UI manages message delivery
- Priority System: Admin commands (SOURCE=admin) have absolute priority
- Collaboration: Agents discuss and confirm execution plans for admin tasks
Detailed Workflow
Initial Setup:
- Start Controller UI with
python main_controller.py
- Open Cursor with two chat tabs
- Activate Agent 1 in Tab 1:
start agent chat 1
- Activate Agent 2 in Tab 2:
start agent chat 2
- Verify both agents appear in Controller UI "Waiting Agents" section
Message Communication:
- Send message from Agent 1 (will appear in message queue)
- Use Controller UI to route message to Agent 2
- Agent 2 receives and can respond
- Continue conversation through Controller UI routing
Admin Controls:
- Send admin messages with absolute priority
- Use "Smart Delivery" for automatic routing
- Monitor real-time agent status
- Clear data when needed
Admin Controls
- Absolute Authority: Admin messages override all agent activities
- Smart Delivery: Automatic routing to available agents
- Manual Routing: Precise control over message delivery
- Real-time Monitoring: Live status of waiting agents and message queue
šļø Project Structure
Utils/
āāā agent_comm/
ā āāā core/ # Core system components
ā ā āāā config_manager.py # Configuration management
ā ā āāā flow_manager.py # Message flow control
ā ā āāā message_handler.py # Message processing
ā ā āāā state_manager.py # System state management
ā āāā ui/ # User interface components
ā ā āāā controller_ui.py # Main controller interface
ā ā āāā styles.py # UI styling
ā āāā chat_ui/ # Chat interface system
ā āāā shared_data/ # Persistent data storage
āāā mcp_server_agent1.py # Agent 1 MCP server
āāā mcp_server_agent2.py # Agent 2 MCP server
āāā rule_for_AI_VI.txt # Vietnamese rules
āāā rule_for_AI_EN.txt # English rules
āāā README.md # This file
šÆ Key Components
Agent Chat Tools
- mcp_agent_chat_1_agent_chat_1_tool: Communication tool for Agent 1
- mcp_agent_chat_2_agent_chat_2_tool: Communication tool for Agent 2
Controller Features
- Message queue management
- Agent status monitoring
- Smart delivery system
- File and image attachment support
- Real-time refresh capability
Rule System
- SOURCE Tag Authority: admin = absolute priority, agent = standard
- Initialization Rules: Keyword-based activation system
- Workflow Compliance: Mandatory tool recall and thinking blocks
- Language Consistency: Vietnamese or English throughout communication
š§ Advanced Features
Message Types
- Text Messages: Standard communication
- File Attachments: Document and code sharing
- Image Support: Visual content communication
- Mixed Content: Combined text, files, and images
Priority System
- Admin Commands: Immediate execution, override all activities
- Agent Messages: Standard peer-to-peer communication
- Collaboration Required: Agents must discuss admin task execution
UI Controller
- Real-time Updates: 1.5-second refresh intervals
- Multi-selection: Batch operations on messages
- Smart Routing: Automatic agent selection
- Status Tracking: Comprehensive system monitoring
š Troubleshooting
Common Issues
-
MCP Server Not Starting
- Verify Python path in configuration
- Check file permissions
- Ensure all dependencies are installed
-
Agents Not Communicating
- Confirm both agents are registered
- Check controller UI for waiting agents
- Verify rule file is properly configured
-
Message Queue Issues
- Use "Clear All Data" in controller UI
- Restart MCP servers
- Check shared_data directory permissions