This project demonstrates a straightforward MCP client-server setup, allowing large language models to interact with external tools and APIs through a standardized protocol, facilitating secure multi-channel communication.
Added on:
Created by:
May 07 2025
This project demonstrates a straightforward MCP client-server setup, allowing large language models to interact with external tools and APIs through a standardized protocol, facilitating secure multi-channel communication.
Added on:
Created by:
May 07 2025
Sofian Hadiwijaya
Featured
What is Basic implementation MCP client server?
The MCP (Model Context Protocol) client-server Python project enables LLMs to seamlessly connect with external tools, APIs, and resources via a secure and extensible protocol. It includes an MCP server that exposes tools via SSE and an MCP client that interacts with the server, utilizing OpenAI models for processing. Designed for developers, this setup supports advanced workflows, tool integration, and secure communications, making AI-human interactions more versatile and powerful in various applications.
Who will use Basic implementation MCP client server?
- Developers working with AI integrations
- Researchers in language model protocols
- Organizations implementing AI-enabled tools
How to use the Basic implementation MCP client server?
- Step1: Install dependencies with uv sync
- Step2: Set environment variables in `.env` file
- Step3: Start the MCP server using `uv run server.py`
- Step4: Run the MCP client with `uv run client.py`
- Step5: Interact through the client prompt for queries and tool usage
Basic implementation MCP client server's Core Features & Benefits
The Core Features
- Exposes tools via SSE
- Connects to OpenAI models for query processing
- Supports multi-channel communication
The Benefits
- Secure and standardized tool integration
- Real-time communication with external resources
- Supports extensible workflows for AI interactions
Basic implementation MCP client server's Main Use Cases & Applications
- AI-powered chatbots with external API integration
- Research workflows for language model protocol testing
- Organizations deploying intelligent automation tools
FAQs of Basic implementation MCP client server
Developer
You may also like:
Developer Tools
A desktop application for managing server and client interactions with comprehensive functionalities.
A Model Context Protocol server for Eagle that manages data exchange between Eagle app and data sources.
A chat-based client that integrates and uses various MCP tools directly within a chat environment for enhanced productivity.
A Docker image hosting multiple MCP servers accessible through a unified entry point with supergateway integration.
Provides access to YNAB account balances, transactions, and transaction creation through MCP protocol.
A fast, scalable MCP server for managing real-time multi-client Zerodha trading operations.
A remote SSH client facilitating secure, proxy-based access to MCP servers for remote tool utilization.
A Spring-based MCP server integrating AI capabilities for managing and processing Minecraft mod communication protocols.
A minimalistic MCP client with essential chat features, supporting multiple models and contextual interactions.
A secure MCP server enabling AI agents to interact with Authenticator App for 2FA codes and passwords.
Research And Data
A server implementation supporting Model Context Protocol, integrating CRIC's industrial AI capabilities.
Provides real-time traffic, air quality, weather, and bike-sharing data for Valencia city in a unified platform.
A React application demonstrating integration with Supabase via MCP tools and Tambo for UI component registration.
A MCP client integrating Brave Search API for web searches, utilizing MCP protocol for efficient communication.
A protocol server enabling seamless communication between Umbraco CMS and external applications.
NOL integrates LangChain and Open Router to create a multi-client MCP server using Next.js
Connects LLMs to Firebolt Data Warehouse for autonomous querying, data access, and insight generation.
A client framework for connecting AI agents to MCP servers, enabling tool discovery and integration.
Spring Link facilitates linking and managing multiple Spring Boot applications efficiently within a unified environment.
An open-source client to interact with multiple MCP servers, enabling seamless tool access for Claude.
AI Chatbot
Integrates APIs, AI, and automation to enhance server and client functionalities dynamically.
Provides long-term memory for LLMs by storing and retrieving contextual information via MCP standards.
An advanced clinical evidence analysis server supporting precision medicine and oncology research with flexible search options.
A platform collecting A2A agents, tools, servers, and clients for effective agent communication and collaboration.
A Spring-based chatbot for Cloud Foundry that integrates with AI services, MCP, and memGPT for advanced capabilities.
An AI agent controlling macOS using OS-level tools, compatible with MCP, facilitating system management via AI.
PHP client library enabling interaction with MCP servers via SSE, StdIO, or external processes.
A platform for managing and deploying autonomous agents, tools, servers, and clients for automation tasks.
Enables interaction with powerful Text to Speech and video generation APIs for multimedia content creation.
An MCP server providing API access to RedNote (XiaoHongShu, xhs) for seamless integration.