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Mkc909 Agent Communication MCP Server
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Mkc909 Agent Communication MCP Server

The Cross-System Agent Communication MCP server is used to manage the collaboration, task allocation, and knowledge sharing of multi-role AI agents, integrating GitHub and database functions.
Artificial intelligence chatbotsDeveloper tools#Agent Collaboration#Task Management#Cross-System Communication#GitHub Integration.TypeScript
2 points
6.7K
Open Site

Overview

Content Details

Alternatives

What is the MCP server?

The MCP server is an intelligent agent collaboration platform that allows AI agents with different functions (such as different modes of Roo) to register as team members, communicate via a message bus, share context information, and collaborate on complex tasks.

How to use the MCP server?

First, register your AI agent. Then, you can communicate with other agents by sending messages, create and assign tasks, or share context information. The server provides a complete API for program calls.

Applicable scenarios

Suitable for scenarios that require collaboration among multiple AI agents, such as: 1) Decomposition and solution of complex problems; 2) Coordination of cross-system workflows; 3) Knowledge sharing and team learning; 4) Automated project management.

Main Features

Agent Registration and Management
Allows AI agents with different functions to register to the system and records their capabilities and status.
Message Bus
Provides an asynchronous communication channel between agents, supporting message sending, receiving, and management.
Task Coordination
Creates, assigns, and tracks task progress, enabling work collaboration between agents.
Context Sharing
Allows agents to share knowledge and context information, improving collaboration efficiency.
GitHub Integration
Directly creates and manages GitHub issues, pull requests, and projects.
PlanetScale Integration
Uses a high-performance database to store agent data, messages, and task records.
Advantages
Enables seamless collaboration among different AI agents
Improves the ability to solve complex problems through task decomposition
Avoids redundant work through knowledge sharing
Deeply integrates with development tools such as GitHub
An extensible architecture supports a large number of agents
Limitations
Requires some setup and configuration work
There may be delays in communication between agents
Clear agent roles and protocols need to be defined initially
Depends on external services (GitHub, PlanetScale)

How to Use

Installation Preparation
Ensure that Node.js 18+ and TypeScript 5.3+ are installed, and prepare your GitHub and PlanetScale accounts.
Environment Configuration
Create a.env file to configure your GitHub token and PlanetScale connection information.
Start the Server
Install dependencies and start the MCP server.
Register the First Agent
Register your AI agent via the API and specify its name and capability description.

Usage Examples

Collaborative Development of New Features
The product manager agent creates a requirement task, assigns it to the development agent for implementation, the code review agent reviews the code, and finally the deployment agent completes the release.
Problem Troubleshooting and Repair
After the monitoring agent discovers a problem, it creates an issue. The diagnostic agent analyzes the cause, the repair agent submits a PR, and the testing agent verifies the repair.
Knowledge Sharing and Learning
New agents can quickly acquire team knowledge by querying the context, avoiding redundant learning.

Frequently Asked Questions

How many agents can the MCP server support working simultaneously?
What if there are delays in communication between agents?
How to ensure message security?
Can non-AI systems be integrated?
Is there a backup for the data stored in PlanetScale?

Related Resources

GitHub Repository
Project source code and latest updates
PlanetScale Documentation
Database usage guide
GitHub API Reference
GitHub integration API documentation
Example Agent Implementations
Reference implementations of various types of agents

Installation

Copy the following command to your Client for configuration
Note: Your key is sensitive information, do not share it with anyone.

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