MCP Servers For Developers

This document categorizes and organizes various Model Context Protocol (MCP) servers to help developers integrate AI models with resources such as databases, file systems, APIs, cloud services, and automation tools.

Developer tools Artificial intelligence chatbots #File System #Version Control #Database #AI Service

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update time : 2025-04-23

What is an MCP Server?

The MCP (Model Context Protocol) server is a middleware tool designed to help developers interact with local or remote resources through AI models. These resources include file systems, databases, cloud storage, development tools, etc.

How to Use an MCP Server?

Users can connect to the MCP server through simple configuration and commands, and then access various resources and perform operations. For example, uploading files, querying databases, or controlling automated tasks.

Applicable Scenarios

The MCP server is suitable for scenarios that require the integration of AI models with multiple types of resources, such as enterprise-level application development, data analysis, and optimization of automated processes.

Main Features

File System AccessAllows AI models to directly access local or remote file systems, supporting reading, writing, and managing files.

Database QuerySupports the connection and operation of multiple databases (such as PostgreSQL and MySQL), providing SQL query and data analysis capabilities.

Version Control System IntegrationSupports seamless docking with version control systems such as Git, GitHub, and GitLab, facilitating code management and collaboration.

AI Service IntegrationProvides support for mainstream AI services such as OpenAI and HuggingFace, enhancing model functionality.

Advantages and Limitations

Advantages

Simplify complex resource configuration and operation processes

Support a unified interface for multiple platforms and various resource types

Improve work efficiency and reduce development costs

Limitations

Some advanced features may require additional configuration

Some services may be restricted by the network environment

How to Use

Install the MCP Server

Download and install the MCP server software, ensuring that the environment meets the minimum requirements.

Configure Connection Parameters

Set the connection information of the target resources according to your needs, such as the database URL and API key.

Test the Connection

Run a test command to verify the connection status with the resources.

Usage Examples

File Upload ExampleUpload a local file to remote storage through the MCP server.

Database Query ExampleRetrieve user data from a PostgreSQL database.

Frequently Asked Questions

Does the MCP server support a multilingual environment?

How to handle the problem of insufficient permissions?

Related Resources

Official Documentation

Detailed installation guides and user manuals

GitHub Code Repository

Source code repository and community discussion area

Tutorial Video

Quick start video demonstration

🚀 MCP Servers for Developers

This document categorizes and organizes various Model Context Protocol (MCP) servers to assist developers in integrating AI models with diverse resources such as databases, file systems, APIs, cloud services, and automation tools.

🚀 Quick Start

This README provides a comprehensive overview of different types of MCP servers, which can help developers integrate AI models with various resources.

✨ Features

Diverse Categories: Covers multiple categories including file systems, version control, databases, cloud services, etc.
Rich Resources: Lists numerous MCP servers with their corresponding functions and links.

📚 Documentation

🔍 Introduction

This document classifies and organizes various Model Context Protocol (MCP) servers to assist developers in integrating AI models with diverse resources such as databases, file systems, APIs, cloud services, and automation tools.

📋 Categories

📂 1. File System

Provide access to local and remote file systems and configure permissions.

Backup Server - Offers file and folder backup and restoration functions for AI agents and code editing tools.
FileSystem - Allows direct access to the local file system and configures permissions for reading, writing, and managing files.
Everything Search - A high-speed file search MCP server that uses the Everything SDK for fast indexing and retrieval on Windows.
llm-context - Provides a way to share local code context with LLMs through the model context protocol or clipboard.

🔄 2. Version Control

Enable interaction with Git and other version control systems.

GitHub - Allows integration with GitHub repositories for repository browsing, issue tracking, and pull request management.
GitLab - Provides GitLab integration, supporting project and repository management, CI/CD operations, and collaboration.
Git - A server that allows direct interaction with Git for code version control and collaborative development.
BifrostMCP - Provides code pull request analysis based on GitHub.

💽 3. Database

Provide interaction with various database systems.

PostgreSQL MCP Server - Enables data query and operation between AI models and PostgreSQL databases.
Redis MCP Server - Provides interaction with Redis key - value storage, supporting data caching and real - time data processing.

☁️ 4. Cloud Services

Enable service calls to major cloud platforms.

AWS MCP Server - Allows AI models to call various Amazon Web Services (AWS) cloud services such as S3 and EC2.
Google Cloud MCP Server - Provides interaction with Google Cloud Platform (GCP), supporting services like BigQuery and Compute Engine.

📢 5. Message Queues

Enable integration with various message queue systems.

RabbitMQ MCP Server - Allows AI models to perform asynchronous communication and task distribution through RabbitMQ.
Kafka MCP Server - Provides integration with Apache Kafka, supporting large - scale real - time data stream processing.

📊 6. Log Management

Enable collection, analysis, and monitoring of system logs.

ELK Stack MCP Server - Integrates the Elasticsearch, Logstash, and Kibana (ELK) stack, supporting query and visualization of structured log data.
Prometheus Logging MCP Server - Provides Prometheus - based log monitoring capabilities, allowing AI models to analyze log data to identify patterns and anomalies.

💬 7. Real - Time Communication

Enable interaction with real - time communication systems.

WebSocket MCP Server - Allows AI models to perform real - time two - way communication through the WebSocket protocol.
Telegram Bot API MCP Server - Provides integration with the Telegram Bot API, supporting message sending and receiving and user interaction.

💳 8. Payment Gateway

Enable support for online payment services.

Stripe MCP Server - Allows AI models to process Stripe payment requests, supporting multiple payment methods such as credit cards and e - wallets.
PayPal MCP Server - Provides integration with the PayPal payment gateway, supporting online transaction processing and payment status query.

🔑 9. Authentication

Enable user authentication and authorization functions.

OAuth2 MCP Server - Allows AI models to perform third - party login and authentication through the OAuth2 protocol.
JWT MCP Server - Provides JSON Web Token (JWT) - based authentication functions, supporting signing, encryption, and token parsing.

🏺 10. Monitoring and Alarming

Enable monitoring of systems and applications and provide alarm functions.

Prometheus Monitoring MCP Server - Provides Prometheus - based monitoring capabilities, allowing AI models to query and analyze system metrics, logs, and alarm conditions to detect performance anomalies.
Grafana Monitoring MCP Server - Integrates Grafana...