Discover Awesome MCP Servers

CS-Cart MCP Server

Integrates with CS-Cart REST API to fetch products and orders, enabling product search and order retrieval for e-commerce management.

Mcpserver

一个 MCP 服务器示例

Binance MCP Server

Provides over 156 tools to interact with the Binance.com global exchange API for spot trading, wallet management, and staking operations. It enables users to execute orders, retrieve market data, and manage crypto assets through natural language interfaces like Claude and ChatGPT.

JP's MCP Collection

A comprehensive utility MCP server that enables AI assistants to execute system commands, manage files, integrate with Google Sheets and Tasks, perform AI-powered text processing, and load dynamic prompts from markdown files.

Enterprise MCP Server

A production-ready Model Context Protocol server that integrates with ServiceNow for enterprise workflows and provides comprehensive health monitoring capabilities.

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.

workday-mcp

Read-only MCP server for Workday that fetches your tasks and data cards (pay, benefits, compensation) via your existing browser session, returning structured JSON.

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.

Civis MCP Server

Enables interaction with the Civis Platform for data exploration, querying, and workflow execution. Supports schema-restricted read-only mode and tools like run_query, list_tables, and publish_html_report.

Coval MCP Server

Enables AI assistants to interact with Coval's evaluation platform for launching and monitoring evaluation runs, managing agents and test sets, and retrieving evaluation metrics.

instantKOM MCP Server

Enables AI assistants to manage instantKOM messenger platform resources (channels, contacts, messages, newsletters, bots, flows, analytics) via structured type-safe tool calls.

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.

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.

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.

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.

micropub-mcp

Enables AI agents to create, update, delete, and query posts on any Micropub-compatible blog with IndieAuth authentication.

LSP MCP Server for Rell

An MCP server for interacting with the Rell Language Server Protocol interface. It enables LLMs to query LSP Hover and Completion providers for Rell projects.

Google News MCP Agent

Fetches news articles from Google News, vectorizes them locally using ChromaDB, and enables semantic search through natural language queries.

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.

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.

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.

GeoRanker MCP Server

An agent-friendly MCP server for the GeoRanker High-Volume API, enabling SEO rank tracking and keyword management through natural language.

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.

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

MCP Atlassian

A Model Context Protocol server for Atlassian Jira and Confluence that supports both Cloud and On-Prem/Data Center deployments. It enables AI assistants to search, create, and manage issues and pages using secure authentication methods like PAT and OAuth.

Agentic Vault

MCP server that lets AI agents call APIs without ever seeing the credentials, using a local encrypted vault and per-secret allowlist policies for HTTP requests and subprocess environment variables.