Explore MCP Server for 2025
Discover the complete collection of MCP servers available in 2025. Find detailed information and the latest updates for each server.
All MCP Server
MCP for Replicate Flux Model - A powerful tool for generating customized images and SVG assets that match specific coding vibes and aesthetic styles. Streamline your visual asset creation process with AI-powered design generation tailored for developers.
This Model Context Protocol (MCP) server provides a connection between Claude and Google Tasks, enabling you to manage your task lists and tasks directly through Claude.
A TypeScript implementation of a Model Context Protocol (MCP) server that integrates with PiAPI's API. PiAPI allows users to generate media content with Midjourney/Flux/Kling/LumaLabs/Udio/Chrip/Trellis directly from Claude or any other MCP-compatible apps.
A MCP Server for the RAG Web Browser Actor
APISIX Model Context Protocol (MCP) server is utilized to connect large language models (LLMs) with the APISIX Admin API.
An MCP server that installs other MCP servers for you
.
Code
```python
import socket
import threading
Server settings
HOST = '127.0.0.1' Localhost
PORT = 12345 Port to listen on
List to keep track of connected clients
clients = []
def handle_client(client_socket, addr):
print(f"Connection from {addr} has been established!")
clients.append(client_socket)
while True:
try:
message = client_socket.recv(1024).decode('utf-8')
if message:
print(f"Received message: {message}")
broadcast(message, client_socket)
else:
break
except:
break
print(f"Connection from {addr} has been closed.")
clients.remove(client_socket)
client_socket.close()
def broadcast(message, client_socket):
for client in clients:
if client != client_socket:
client.send(message.encode('utf-8'))
def start_server():
server_socket = socket.socket(socket.AF_INET, socket.SOCK_STREAM)
server_socket.bind((HOST, PORT))
server_socket.listen()
print(f"Server is listening on {HOST}:{PORT}")
while True:
client_socket, addr = server_socket.accept()
thread = threading.Thread(target=handle_client, args=(client_socket, addr))
thread.start()
if __name__ == "__main__":
start_server()
```
How to Run
1. Save the code in a file named `unichat_server.py`.
2. Open a terminal and navigate to the directory where the file is saved.
3. Run the server using the command:
```
python unichat_server.py
```
Connecting Clients
You can create a simple client using the following code:
```python
import socket
HOST = '127.0.0.1' Server address
PORT = 12345 Server port
client_socket = socket.socket(socket.AF_INET, socket.SOCK_STREAM)
client_socket.connect((HOST, PORT))
while True:
message = input("Enter message: ")
client_socket.send(message.encode('utf-8'))
```
Conclusion
This is a basic implementation of a chat server in Python. You can enhance it by adding features like user authentication, message history, or a graphical user interface.](https://avatars.githubusercontent.com/u/1759588?v=4 "Unichat MCP Server in Python
Overview
Unichat is a simple chat server that allows multiple clients to connect and communicate with each other. This implementation is done using Python's socket programming.
Requirements
- Python 3.x
- Basic knowledge of socket programming
Installation
Make sure you have Python installed on your machine. You can download it from [python.org](https://www.python.org/downloads/).
Code
```python
import socket
import threading
Server settings
HOST = '127.0.0.1' Localhost
PORT = 12345 Port to listen on
List to keep track of connected clients
clients = []
def handle_client(client_socket, addr):
print(f\"Connection from {addr} has been established!\")
clients.append(client_socket)
while True:
try:
message = client_socket.recv(1024).decode('utf-8')
if message:
print(f\"Received message: {message}\")
broadcast(message, client_socket)
else:
break
except:
break
print(f\"Connection from {addr} has been closed.\")
clients.remove(client_socket)
client_socket.close()
def broadcast(message, client_socket):
for client in clients:
if client != client_socket:
client.send(message.encode('utf-8'))
def start_server():
server_socket = socket.socket(socket.AF_INET, socket.SOCK_STREAM)
server_socket.bind((HOST, PORT))
server_socket.listen()
print(f\"Server is listening on {HOST}:{PORT}\")
while True:
client_socket, addr = server_socket.accept()
thread = threading.Thread(target=handle_client, args=(client_socket, addr))
thread.start()
if __name__ == \"__main__\":
start_server()
```
How to Run
1. Save the code in a file named `unichat_server.py`.
2. Open a terminal and navigate to the directory where the file is saved.
3. Run the server using the command:
```
python unichat_server.py
```
Connecting Clients
You can create a simple client using the following code:
```python
import socket
HOST = '127.0.0.1' Server address
PORT = 12345 Server port
client_socket = socket.socket(socket.AF_INET, socket.SOCK_STREAM)
client_socket.connect((HOST, PORT))
while True:
message = input(\"Enter message: \")
client_socket.send(message.encode('utf-8'))
```
Conclusion
This is a basic implementation of a chat server in Python. You can enhance it by adding features like user authentication, message history, or a graphical user interface."){kind=small}
A Desktop Chat App that uses MCP (Model Context Protocol) to connect with other LLMs (Large Language Models).
Analyzes your codebase by identifying important files based on dependency relationships. Generates diagrams and importance scores for each file, assisting AI assistants in understanding the codebase. Automatically parses popular programming languages such as Python, C, C++, Rust, Zig, and Lua.
Mcp Google Search
A MCP for Claude Desktop / Claude Code / Windsurf / Cursor to build n8n workflows for you
A Nostr MCP server that allows interaction with Nostr, enabling the posting of notes and more.
A Model Context Protocol (MCP) server implementation connecting Claude Desktop with DeepSeek's language models (R1/V3)
📦 Repomix is a powerful tool that combines your entire repository into one single file that is friendly for AI. It is ideal for when you need to provide your codebase to Large Language Models (LLMs) or other AI tools like Claude, ChatGPT, DeepSeek, Perplexity, Gemini, Gemma, Llama, Grok, and more.
