Discover Awesome MCP Servers

Slice.js Documentation MCP

Enables AI assistants to search, list, and retrieve documentation from the Slice.js official GitHub repository. It provides full-text search capabilities and can deliver individual doc pages or a complete documentation bundle for comprehensive LLM context.

MonadKit MCP

Turns Base44's AI builder into a Monad web3 app builder, enabling anyone to vibecode consumer crypto apps (invisible wallets, gasless USDC payments) by describing them in English.

Jira & Confluence MCP Server

Enables interaction with Jira and Confluence APIs to search, create, and manage issues, pages, comments, and attachments across both Atlassian platforms.

bilibili-subtitle-fetch

A Bilibili subtitle fetching MCP server that supports various input formats (BV number, video URL, short link) and offers configurable language preferences, output formats, and ASR fallback.

ArmBench MCP Server

Enables benchmarking and inference of LLMs on Arm64 cloud instances with KleidiAI optimizations, providing an MCP-compatible API for serving results.

Larry

Social coding platform for AI agents. Post snippets, fork code, vote, follow agents, build reputation. REST API + MCP server.

MSFS SDK MCP Server

A modern MCP server that provides fast, structured access to Microsoft Flight Simulator SDK documentation through natural language and structured queries.

@bldbl/mcp

This package enables AI assistants (Claude, GPT, etc.) to work directly with Buildable projects using the Model Context Protocol (MCP). AI assistants can get project context, manage tasks and track progress (for projects created at https://bldbl.dev).

DoiT MCP Server

DoiT 官方 MCP 服务器 (DoiT guānfāng MCP fúwùqì)

SQL Server Analysis Services MCP Server by CData

SQL Server Analysis Services MCP Server by CData

Picsha AI MCP Server

MCP server for Picsha AI platform enabling LLMs to search, upload, and manage digital assets through natural language, with secure local proxy and multi-tenant sandbox support.

boss-agent-cli

Local MCP server for BOSS Zhipin workflows. Exposes 49 tools for job search, welfare filtering, recruiter messaging, pipeline tracking, and resume optimization for AI agents.

dida-mcp-server

Enables AI assistants to manage TickTick/Dida365 tasks, projects, and tags through the MCP protocol, with features for GTD-based task organization and OAuth authentication.

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.

Slack MCP Server with SSE Transport

带有 SSE 传输的 Slack MCP 服务器

TestMCP

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

vulcan-file-ops

MCP server that gives Claude Desktop and other desktop MCP clients filesystem powers—read, write, edit, and manage files like AI coding assistants.

Hindenrank MCP Server

Enables users to check crypto protocol risk ratings, search for DeFi protocols, and compare safety profiles using Hindenrank data. It provides detailed risk grades, top risks, and protocol verdicts directly through MCP-compatible clients.

shipcheck-mcp

Enables AI agents to run Shipcheck on local JavaScript/TypeScript repositories, scanning for launch risks like exposed env vars, unsigned webhooks, and missing security guardrails.

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.**

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.

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.

web-browser-mcp

Enables AI agents to perform query-driven web searches and fetch page content via Bing and DuckDuckGo engines, with automatic fallback and no API keys needed.

ZMCPTools

A multi-agent orchestration platform for Claude Code providing 61 tools for autonomous agent coordination, browser automation, and documentation intelligence. It features LanceDB-powered semantic search, knowledge graph memory systems, and advanced task management for complex development workflows.

Easyship MCP

Enables AI agents to manage global shipping operations, including rate comparison, shipment creation, label purchasing, tracking, pickup scheduling, address validation, billing, and analytics, via natural language.

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.

InstaDomain

Domain registration for AI agents. Check availability, buy domains (Stripe or x402 crypto), and auto-configure Cloudflare DNS - all without leaving your coding session.

Remote MCP Server (Authless)

A template for deploying authentication-free MCP servers on Cloudflare Workers. Enables quick deployment of custom tools accessible via SSE from MCP clients like Claude Desktop or Cloudflare AI Playground.

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 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.