Discover Awesome MCP Servers

LogixDesigner-MCP

MCP server wrapping the Rockwell Automation Logix Designer SDK to open ACD files, export to L5K/L5X, read rung logic, and enumerate tags and program structure via typed MCP tools, bridged from Windows to Linux via Cloudflare Tunnel.

Interzoid Weather City API MCP Server

An MCP server that provides access to the Interzoid GetWeatherCity API, allowing users to retrieve weather information for specified cities through natural language interactions.

google-spreadsheet-mcp

MCP server enabling AI agents to read, write, and manage Google Sheets, including sheet manipulation, row/column operations, dropdowns, checkboxes, and conditional formatting.

Civil3D MCP Server

Enables AI assistants to interact with Autodesk Civil 3D, allowing them to retrieve project data, create/modify/delete drawing elements, and execute code to automate Civil 3D operations.

MCP Firebird

Một máy chủ triển khai Giao thức Ngữ cảnh Mô hình (MCP) của Anthropic cho cơ sở dữ liệu Firebird SQL, cho phép Claude và các LLM khác truy cập, phân tích và thao tác dữ liệu một cách an toàn trong cơ sở dữ liệu Firebird thông qua ngôn ngữ tự nhiên.

claude-memory

Cross-machine memory system for Claude Code that records sessions as searchable markdown, syncs across machines via Git, and exposes full-text search, semantic search, session summarization, and a knowledge graph through an MCP server.

CloakMCP

An MCP server that provides LLMs with stealth browser automation capabilities via CloakBrowser to bypass bot detection services like Cloudflare and reCAPTCHA. It supports full page interaction, content extraction, and human-like behavior through 30 specialized tools.

agent-recall

Enables persistent memory for AI coding agents, allowing them to remember people, decisions, and context across sessions through a knowledge graph and automated briefings.

agentguard

Enables scanning of AI agent code for security vulnerabilities such as prompt injection, tool abuse, and data exfiltration, directly from MCP-compatible clients like Claude Code.

ToolMux

Aggregates multiple MCP servers via a single interface with token optimization and multiple operating modes (Gateway, Meta, Proxy, Search, Code).

Amazon Product Search MCP

Enables AI-powered Amazon product searches and recommendations by integrating the Amazon API with Hugging Face models. It allows users to filter products by price and specific features to receive tailored shopping suggestions.

Radar de Riesgo de Devolución

MCP server for e-commerce return risk analysis, providing tools to calculate customer risk profiles, compare segments, and identify risk factors, with memory for contextual conversations.

SQLite Project Memory MCP

A graph-friendly relational server that stores project memory, tasks, and metadata in a centralized SQLite database as the authoritative source of truth. It enables AI agents to manage complex project states through entity-relationship modeling and can generate human-readable markdown views on demand.

ytmcp

Enables AI assistants to fetch YouTube video transcripts with precise timestamps, multi-language support, and time-range filtering.

readypermit-mcp

AI-powered property intelligence for instant zoning analysis, buildability assessments, ADU eligibility, flood risk, and development feasibility reports for any US address.

IoT Device Management MCP Server

Enables registration, monitoring, and control of IoT devices via AI agents, with local storage and no cloud API key required.

phase8-mcp

MCP server for the Korg Phase 8 acoustic synthesizer that enables triggering resonators, controlling per-resonator knobs, and modulating global parameters over USB MIDI.

ellmos-servercommander-mcp

Alpha MCP server for server operations enabling deployment dry-runs, mail readiness diagnostics, access-log analysis, and HTTP health checks.

paraph-mcp

MCP server for the Paraph e-signature API that enables AI tools to fill PDF forms and manage electronic signing workflows. It provides tools for template management, document filling, sending signing requests, and tracking signing progress.

reddit-trends-mcp

Provides Reddit discussion volume trends, growth rates, and top trending topics for any keyword, accessible via MCP tools and Python client.

Commodore 64 Ultimate MCP Server

Enables AI assistants to control Commodore 64 Ultimate hardware via REST API, supporting program execution, memory operations, disk management, audio playback, and device configuration through natural language commands.

MCP Server for Odoo

Enables AI assistants to interact with Odoo ERP systems through natural language, allowing users to search, create, update, and manage business records like customers, products, and invoices across any Odoo instance.

unstuck-mcp

Prevents coding agents from repeatedly attempting the same failed fix by tracking attempts and blocking further fixes until the agent uses its own web search tool.

wows-remote-agent

MCP server to remotely monitor and control a Windows PC running World of Warships via Tailscale, enabling status checks, screenshots, game launch, and calibrated menu workflows with safety limits.

A MCP server for Godot RAG

Máy chủ MCP này được sử dụng để cung cấp tài liệu Godot cho mô hình Godot RAG.

FFmpeg MCP

Enables video and audio processing through FFmpeg, supporting format conversion, compression, trimming, audio extraction, frame extraction, video merging, and subtitle burning through natural language commands.

mcp-guard

Zero-dependency local proxy that wraps any MCP server to redact secrets, strip hidden-Unicode prompt injection, and block writes to protected paths like ~/.ssh and .env.

discord-mcp-server

Lets any MCP-compatible AI client interact with Discord — send messages, manage channels, create webhooks, assign roles, and more.

Cars MCP Server

Okay, here's a basic example of how you might set up an MCP (Message Channel Platform) server using Spring AI, along with explanations to help you understand the key components. This example focuses on the core concepts and assumes you have a basic understanding of Spring Boot and Spring AI. **Conceptual Overview** The idea is to create a simple server that: 1. **Receives Messages:** Accepts messages from clients (e.g., via HTTP). 2. **Uses Spring AI:** Leverages Spring AI to process the message (e.g., generate a response, extract information). 3. **Sends a Response:** Returns a response to the client. **Code Example (Simplified)** ```java // Dependencies (pom.xml or build.gradle) // - spring-boot-starter-web // - spring-ai-spring-boot-starter (and the specific AI provider you want, e.g., OpenAI) import org.springframework.ai.client.AiClient; import org.springframework.ai.prompt.PromptTemplate; import org.springframework.beans.factory.annotation.Autowired; import org.springframework.boot.SpringApplication; import org.springframework.boot.autoconfigure.SpringBootApplication; import org.springframework.web.bind.annotation.PostMapping; import org.springframework.web.bind.annotation.RequestBody; import org.springframework.web.bind.annotation.RestController; import java.util.Map; @SpringBootApplication public class McpServerApplication { public static void main(String[] args) { SpringApplication.run(McpServerApplication.class, args); } } @RestController class MessageController { @Autowired private AiClient aiClient; @PostMapping("/message") public String processMessage(@RequestBody String userMessage) { // 1. Create a prompt for the AI model. This is crucial! String promptTemplateText = "You are a helpful assistant. The user's message is: {userMessage}"; PromptTemplate promptTemplate = new PromptTemplate(promptTemplateText); Map<String, Object> model = Map.of("userMessage", userMessage); // 2. Call the AI model using Spring AI. String response = aiClient.generate(promptTemplate.render(model)); // 3. Return the AI's response. return response; } } ``` **Explanation:** 1. **Dependencies:** Make sure you have the necessary dependencies in your `pom.xml` (Maven) or `build.gradle` (Gradle) file. The key ones are: * `spring-boot-starter-web`: For creating a web server (handling HTTP requests). * `spring-ai-spring-boot-starter`: The core Spring AI starter. * `spring-ai-openai-spring-boot-starter` (or similar): A starter for a specific AI provider (e.g., OpenAI, Azure OpenAI, Ollama). You'll need to choose one and configure it. 2. **`McpServerApplication`:** This is the main Spring Boot application class. It's responsible for starting the Spring Boot application. 3. **`MessageController`:** * `@RestController`: Marks this class as a REST controller, meaning it handles incoming HTTP requests. * `@Autowired private AiClient aiClient;`: This injects the `AiClient` bean, which is the main interface for interacting with the AI model. Spring AI automatically configures this based on your chosen AI provider. * `@PostMapping("/message")`: This maps the `/message` endpoint to the `processMessage` method. It handles HTTP POST requests to this endpoint. * `@RequestBody String userMessage`: This extracts the message sent in the body of the HTTP request and binds it to the `userMessage` variable. * **Prompt Engineering:** This is the most important part. The `promptTemplateText` defines the prompt that will be sent to the AI model. It includes a placeholder `{userMessage}` where the user's message will be inserted. Good prompt engineering is crucial for getting good results from the AI model. * `PromptTemplate promptTemplate = new PromptTemplate(promptTemplateText);`: Creates a `PromptTemplate` object from the text. * `Map<String, Object> model = Map.of("userMessage", userMessage);`: Creates a map to hold the values that will be substituted into the prompt template. * `String response = aiClient.generate(promptTemplate.render(model));`: This is where the magic happens. It calls the `generate` method of the `AiClient` to send the prompt to the AI model and get a response. `promptTemplate.render(model)` fills in the placeholders in the prompt with the actual values. * `return response;`: Returns the AI's response as the HTTP response. **Configuration (application.properties or application.yml)** You'll need to configure Spring AI with your chosen AI provider's credentials. Here's an example for OpenAI: ```properties spring.ai.openai.api-key=YOUR_OPENAI_API_KEY ``` Replace `YOUR_OPENAI_API_KEY` with your actual OpenAI API key. You'll get this from the OpenAI website after creating an account. The exact configuration properties will vary depending on the AI provider you choose. **How to Run It** 1. **Create a Spring Boot project:** Use Spring Initializr (start.spring.io) to create a new Spring Boot project with the necessary dependencies (Web, Spring AI, and your chosen AI provider). 2. **Copy the code:** Copy the code above into your project. 3. **Configure your AI provider:** Add the configuration properties to your `application.properties` or `application.yml` file. 4. **Run the application:** Run the Spring Boot application. 5. **Send a message:** Use a tool like `curl` or Postman to send a POST request to `http://localhost:8080/message` with a JSON body containing your message. For example: ```bash curl -X POST -H "Content-Type: text/plain" -d "Hello, can you tell me a joke?" http://localhost:8080/message ``` **Important Considerations and Improvements** * **Error Handling:** Add error handling to catch exceptions that might occur during AI processing (e.g., API errors, rate limits). * **Prompt Engineering:** Experiment with different prompts to get the best results from the AI model. The prompt is the key to controlling the AI's behavior. * **Security:** If you're handling sensitive data, implement proper security measures (authentication, authorization, encryption). * **Asynchronous Processing:** For more complex scenarios, consider using asynchronous processing (e.g., Spring's `@Async` annotation or a message queue) to avoid blocking the main thread. * **Data Validation:** Validate the incoming messages to prevent malicious input. * **Logging:** Add logging to track requests, responses, and errors. * **More Complex Data Structures:** Instead of just sending a plain string, you can send more complex JSON objects in the request body and process them in the `processMessage` method. This allows you to pass more structured information to the AI model. * **Streaming:** For long responses, consider using Spring AI's streaming capabilities to send the response to the client in chunks. This can improve the user experience. **Vietnamese Translation of Key Concepts** * **MCP (Message Channel Platform):** Nền tảng kênh tin nhắn * **Spring AI:** Spring AI (Không dịch, giữ nguyên tên) * **AI Model:** Mô hình AI * **Prompt:** Lời nhắc, mồi (trong ngữ cảnh AI) * **Prompt Engineering:** Kỹ thuật tạo lời nhắc, kỹ thuật mồi * **API Key:** Khóa API * **Endpoint:** Điểm cuối (API) * **Request Body:** Nội dung yêu cầu (HTTP) * **Response:** Phản hồi * **Asynchronous Processing:** Xử lý bất đồng bộ * **Message Queue:** Hàng đợi tin nhắn * **Authentication:** Xác thực * **Authorization:** Ủy quyền * **Encryption:** Mã hóa * **Data Validation:** Xác thực dữ liệu * **Logging:** Ghi nhật ký This example provides a starting point for building a basic MCP server with Spring AI. Remember to adapt it to your specific needs and requirements. Good luck!

@droplinkperformance/bitbucket-mcp-server

Enables AI-powered pull request review and analysis for Bitbucket Cloud, plus tools to list, create, diff, comment on, and analyze PRs. Supports stdio or HTTP transports with OAuth/bearer auth and pluggable LLM providers.