发现优秀的 MCP 服务器

MCP Server with External Tools

Enables AI models to access external services including weather data, file system operations, and SQLite database interactions through a standardized JSON-RPC interface. Features production-ready architecture with security, rate limiting, and comprehensive error handling.

Notes MCP

An MCP server that enables AI assistants like Claude to access and manipulate Apple Notes on macOS, allowing for retrieving, creating, and managing notes through natural language interactions.

Brand-to-Theme MCP Server

用于将品牌标识 PDF 转换为 Shopify 主题的 MCP 服务器

GitHub Repository Manager MCP Server

Enables AI assistants to create and delete GitHub repositories with customizable settings through the Model Context Protocol, supporting both public and private repositories with secure token-based authentication.

MCP Servers

Here are my MCP servers and their support structure: (This is a direct translation, but depending on the context, you might want to add more nuance. For example, if you're talking about Minecraft servers, you might want to specify that.) 这是我的MCP服务器，以及它们的支持结构。 (Zhè shì wǒ de MCP fúwùqì, yǐjí tāmen de zhīchí jiégòu.) **Possible alternative, if you're talking about Minecraft servers:** 这是我的Minecraft服务器，以及它们的支持结构。 (Zhè shì wǒ de Minecraft fúwùqì, yǐjí tāmen de zhīchí jiégòu.)

Tripo3D MCP

A tool integration that wraps Tripo3D API capabilities for 3D model generation, texturing, animation, and format conversion, supporting text/image-to-3D workflows via natural language commands.

SQL Server Analysis Services MCP Server by CData

SQL Server Analysis Services MCP Server by CData

MCP GitLab Server

Enables comprehensive GitLab integration allowing LLMs to manage projects, issues, merge requests, repository files, CI/CD pipelines, and perform batch operations. Supports advanced features like AI-optimized summaries, smart diffs, and atomic operations with rollback support.

MinIO MCP Server

Enables interaction with MinIO object storage through a standardized Model-Context Protocol interface. Supports listing buckets and objects, retrieving files, and uploading data to MinIO storage.

TestMCP

A Python MCP server that provides a tool called addNumbers for mathematical operations.

Firecrawl MCP Server

一个模型上下文协议服务器，它使 AI 助手能够通过 Firecrawl API 执行高级网络抓取、爬行、搜索和数据提取。

mcp-see

Enables AI agents to analyze images through vision AI providers (Gemini, OpenAI, Claude), performing tasks like image description, object detection with bounding boxes, region-specific analysis, and precise color extraction without consuming context window with raw pixels.

PR Review MCP Server

Enables management of GitHub pull request review threads through natural language, allowing users to list, reply to, and resolve PR review comments using GitHub's GraphQL API.

CodeGuard MCP Server

Provides centralized security instructions for AI-assisted code generation by matching context-aware rules to the user's programming language and file patterns. It ensures generated code adheres to security best practices without requiring manual maintenance of instruction files across individual repositories.

Aseprite MCP Tools

A Python MCP server enabling programmatic interaction with Aseprite for pixel art creation and manipulation with features like drawing operations, palette management, and batch processing.

Omise MCP Server

Enables comprehensive payment processing through Omise APIs including charges, customers, transfers, refunds, disputes, recurring payments, and webhooks. Provides 51 tools covering all Omise API functionality for secure payment integration.

Controtto

Okay, I understand. I will do my best to translate English text into Chinese, and when presented with Go code, I will attempt to analyze it from the perspective of Domain-Driven Design (DDD) and Clean Architecture principles. **Specifically, when analyzing Go code, I will look for:** * **DDD Aspects:** * **Ubiquitous Language:** Is the code using terminology that aligns with the business domain? Are the names of variables, functions, and types meaningful to domain experts? * **Entities:** Are there well-defined entities with identity and behavior? * **Value Objects:** Are there immutable value objects representing domain concepts? * **Aggregates:** Are aggregates used to enforce consistency and manage transactions? Are aggregate roots clearly defined? * **Domain Services:** Are domain services used to encapsulate complex domain logic that doesn't naturally belong to an entity or value object? * **Repositories:** Are repositories used to abstract data access and persistence? * **Domain Events:** Are domain events used to decouple different parts of the system and react to changes in the domain? * **Bounded Contexts:** (If applicable) Is the code organized into bounded contexts with clear boundaries and responsibilities? * **Clean Architecture Aspects:** * **Dependency Inversion Principle (DIP):** Are high-level modules not dependent on low-level modules? Are abstractions used to decouple layers? * **Interface Segregation Principle (ISP):** Are interfaces small and focused, avoiding unnecessary dependencies? * **Single Responsibility Principle (SRP):** Do classes/modules have a single, well-defined responsibility? * **Layers:** Are there distinct layers (e.g., presentation, application, domain, infrastructure)? * **Use Cases/Interactors:** Are use cases clearly defined and implemented as interactors? * **Entities (Domain Layer):** Is the core business logic (entities and domain rules) independent of frameworks and infrastructure? * **Frameworks & Drivers (Outer Layer):** Are frameworks and infrastructure concerns kept separate from the core business logic? * **Testability:** Is the code easily testable, with clear separation of concerns? **My analysis will be strict and critical.** I will point out potential violations of these principles and suggest improvements. I will also consider the trade-offs involved in applying these principles, as strict adherence is not always practical or beneficial. **Important Considerations:** * **Context is Key:** My analysis will be limited by the information you provide. The more context you give me about the domain, the requirements, and the overall architecture, the better I can assess the code. * **Subjectivity:** DDD and Clean Architecture are not rigid rules, but rather guidelines. There is room for interpretation and different approaches. My analysis will reflect my understanding of these principles, which may differ from yours. * **Code Completeness:** I can only analyze the code you provide. I cannot infer the entire system architecture or domain model from a small snippet. **Example:** If you provide me with the following Go code: ```go package main import "fmt" type User struct { ID int Name string Email string } func SaveUser(db *sql.DB, user User) error { _, err := db.Exec("INSERT INTO users (name, email) VALUES (?, ?)", user.Name, user.Email) return err } func main() { // ... database connection ... user := User{Name: "John Doe", Email: "john.doe@example.com"} err := SaveUser(db, user) if err != nil { fmt.Println("Error saving user:", err) } } ``` I might respond with something like: "This code has several potential issues from a DDD and Clean Architecture perspective: * **Lack of Domain Layer:** The `User` struct is directly coupled to the database. It should be part of a dedicated domain layer, separate from infrastructure concerns. * **Anemic Domain Model:** The `User` struct only contains data and lacks behavior. Consider adding methods to the `User` struct that encapsulate domain logic related to users. * **Direct Database Access:** The `SaveUser` function directly interacts with the database. This violates the Dependency Inversion Principle. A repository interface should be used to abstract data access. * **Missing Repository:** There is no repository interface or implementation to handle user persistence. * **No Use Case:** The `main` function directly calls the `SaveUser` function. This should be encapsulated in a use case or interactor. To improve this code, consider: 1. Creating a `domain` package with the `User` entity and any related value objects. 2. Defining a `UserRepository` interface in the `domain` package. 3. Implementing the `UserRepository` interface in an `infrastructure` package, using a database connection. 4. Creating an `application` package with a `CreateUser` use case that uses the `UserRepository` to persist the user. This would result in a more decoupled, testable, and maintainable codebase that adheres to DDD and Clean Architecture principles." **Now, please provide me with the text you want me to translate or the Go code you want me to analyze.**

MCP Workspace Server

Provides secure, sandboxed file system access for AI assistants to read, write, and manage project files with controlled command execution capabilities, all confined to a designated workspace directory.

Zignet

Enables AI-powered Zig programming assistance through code generation, debugging, and documentation explanation. Uses local LLM models to provide idiomatic Zig code creation and analysis capabilities.

Databricks MCP Server

Enables LLM-powered tools to interact with Databricks clusters, jobs, notebooks, SQL warehouses, and Unity Catalog through the Model Completion Protocol. Provides comprehensive access to Databricks REST API functionality including cluster management, job execution, workspace operations, and data catalog operations.

Generate-Prd-Prompt

Mercury Spec Ops MCP Server is a dynamic prompt generation and template assembly tool based on a modular architecture. It is suitable for the interaction between AI assistants and professional content, and supports the dynamic generation of 31 technology stacks, 10 analysis dimensions and 34 templat

Qwen3-Coder MCP Server

Integrates the Qwen3-Coder 30B parameter model with Claude Code through 5 specialized tools for code review, explanation, generation, bug fixing, and optimization. Optimized for 64GB RAM systems with advanced performance settings including flash attention and parallel processing.

Apple RAG MCP

Provides AI agents with instant access to official Apple developer documentation, Swift programming guides, design guidelines, and Apple Developer YouTube content including WWDC sessions. Uses advanced RAG technology with semantic search and AI reranking to deliver accurate, contextual answers for Apple platform development.

Materials Project MCP

A tool for querying and analyzing materials data from the Materials Project database using natural language prompts, enabling materials scientists to explore properties, structures, and compositions of materials through conversational interfaces.

JEFit MCP Server

Enables analysis and retrieval of JEFit workout data through natural language. Provides access to workout dates, detailed exercise information, and batch workout analysis for fitness tracking and progress monitoring.

MCP API Server

A Model Context Protocol server that enables AI assistants to make HTTP requests (GET, POST, PUT, DELETE) to external APIs through standardized MCP tools.

Alibaba Cloud DMS MCP Server

A Model Context Protocol server that enables large language models to access database metadata and perform cross-engine data querying across diverse database ecosystems.