AIBase
EN
Explore
Mkc909 Agent Communication MCP Server
M

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
9.2K
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.

Alternatives

R
Rsdoctor
Rsdoctor is a build analysis tool specifically designed for the Rspack ecosystem, fully compatible with webpack. It provides visual build analysis, multi - dimensional performance diagnosis, and intelligent optimization suggestions to help developers improve build efficiency and engineering quality.
TypeScript
9.4K
5 points
N
Next Devtools MCP
The Next.js development tools MCP server provides Next.js development tools and utilities for AI programming assistants such as Claude and Cursor, including runtime diagnostics, development automation, and document access functions.
TypeScript
9.1K
5 points
T
Testkube
Testkube is a test orchestration and execution framework for cloud-native applications, providing a unified platform to define, run, and analyze tests. It supports existing testing tools and Kubernetes infrastructure.
Go
5.6K
5 points
M
MCP Windbg
An MCP server that integrates AI models with WinDbg/CDB for analyzing Windows crash dump files and remote debugging, supporting natural language interaction to execute debugging commands.
Python
9.2K
5 points
R
Runno
Runno is a collection of JavaScript toolkits for securely running code in multiple programming languages in environments such as browsers and Node.js. It achieves sandboxed execution through WebAssembly and WASI, supports languages such as Python, Ruby, JavaScript, SQLite, C/C++, and provides integration methods such as web components and MCP servers.
TypeScript
8.6K
5 points
P
Praisonai
PraisonAI is a production-ready multi-AI agent framework with self-reflection capabilities, designed to create AI agents to automate the solution of various problems from simple tasks to complex challenges. It simplifies the construction and management of multi-agent LLM systems by integrating PraisonAI agents, AG2, and CrewAI into a low-code solution, emphasizing simplicity, customization, and effective human-machine collaboration.
Python
8.7K
5 points
N
Netdata
Netdata is an open-source real-time infrastructure monitoring platform that provides second-level metric collection, visualization, machine learning-driven anomaly detection, and automated alerts. It can achieve full-stack monitoring without complex configuration.
Go
9.1K
5 points
M
MCP Server
The Mapbox MCP Server is a model context protocol server implemented in Node.js, providing AI applications with access to Mapbox geospatial APIs, including functions such as geocoding, point - of - interest search, route planning, isochrone analysis, and static map generation.
TypeScript
8.2K
4 points
M
Markdownify MCP
Markdownify is a multi-functional file conversion service that supports converting multiple formats such as PDFs, images, audio, and web page content into Markdown format.
TypeScript
32.3K
5 points
N
Notion Api MCP
Certified
A Python-based MCP Server that provides advanced to-do list management and content organization functions through the Notion API, enabling seamless integration between AI models and Notion.
Python
19.1K
4.5 points
D
Duckduckgo MCP Server
Certified
The DuckDuckGo Search MCP Server provides web search and content scraping services for LLMs such as Claude.
Python
65.1K
4.3 points
G
Gitlab MCP Server
Certified
The GitLab MCP server is a project based on the Model Context Protocol that provides a comprehensive toolset for interacting with GitLab accounts, including code review, merge request management, CI/CD configuration, and other functions.
TypeScript
21.8K
4.3 points
U
Unity
Certified
UnityMCP is a Unity editor plugin that implements the Model Context Protocol (MCP), providing seamless integration between Unity and AI assistants, including real - time state monitoring, remote command execution, and log functions.
C#
29.3K
5 points
F
Figma Context MCP
Framelink Figma MCP Server is a server that provides access to Figma design data for AI programming tools (such as Cursor). By simplifying the Figma API response, it helps AI more accurately achieve one - click conversion from design to code.
TypeScript
58.6K
4.5 points
G
Gmail MCP Server
A Gmail automatic authentication MCP server designed for Claude Desktop, supporting Gmail management through natural language interaction, including complete functions such as sending emails, label management, and batch operations.
TypeScript
19.8K
4.5 points
M
Minimax MCP Server
The MiniMax Model Context Protocol (MCP) is an official server that supports interaction with powerful text-to-speech, video/image generation APIs, and is suitable for various client tools such as Claude Desktop and Cursor.
Python
44.3K
4.8 points
AIBase
Zhiqi Future, Your AI Solution Think Tank
English简体中文繁體中文にほんご
© 2026AIBase