Discover Awesome MCP Servers

HashPilot

A Model Context Protocol server that enables AI assistants to interact with Hedera blockchain in real-time, allowing them to check account balances, view block information, and estimate transaction costs.

Brewfather Mcp

MCPサーバーからBrewfatherにアクセスする

Index Network MCP Server

Enables ChatGPT integration with Index Network's discovery protocol through MCP tools. Provides quick setup with ngrok tunneling for public access and includes health check, echo, and search functionality.

MCP Atlassian Server

Integrates with Atlassian Cloud products (Confluence and Jira) to enable AI assistants to search, read, create, and manage pages, issues, comments, attachments, and export content through natural language interactions.

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.

Xiaohongshu MCP Python

A Python implementation of an MCP server for Xiaohongshu that enables AI models to browse feeds, search content, and manage interactions like liking or commenting. It leverages Playwright for browser automation to support account management and content publishing features.

SQL Server Analysis Services MCP Server by CData

SQL Server Analysis Services MCP Server by CData

CDP MCP

A Chrome DevTools Protocol MCP server that enables direct browser automation with auto-discovery of interactive elements, built-in action verification, and persistent site memory across sessions.

QEMU Screenshot MCP Server

Enables AI agents to capture high-quality screenshots from running QEMU virtual machines using the QEMU Machine Protocol (QMP), automatically discovering VMs and returning screenshots as PNG images.

Marketing Master – Landing Page Evaluator

Enables rapid analysis of landing pages for SEO compliance, conversion structure, color contrast accessibility, and performance metrics with automated optimization suggestions and code snippets.

Controtto

Okay, I understand. I can translate English text to Japanese. I can also analyze Go code and provide feedback based on Domain-Driven Design (DDD) and Clean Architecture principles. I will strive to be strict in my evaluation, looking for potential violations and suggesting improvements. To get the best results, please provide me with: * **The English text you want translated to Japanese.** * **The Go code you want me to analyze.** The more context you provide (e.g., a description of the code's purpose, the domain it operates in), the better I can assess it. * **Any specific areas you're concerned about.** For example, "I'm worried about the coupling between the domain and infrastructure layers" or "I'm not sure if this aggregate is correctly defined." **Here's what I'll look for when analyzing Go code under DDD and Clean Architecture:** **Domain-Driven Design (DDD):** * **Ubiquitous Language:** Is the code using terminology that aligns with the domain experts' language? * **Domain Model:** Is there a clear and well-defined domain model that captures the essential concepts and rules of the business? * **Entities:** Are entities correctly identified and modeled with appropriate identity and behavior? * **Value Objects:** Are value objects used to represent immutable concepts and ensure consistency? * **Aggregates:** Are aggregates properly defined with a clear root entity and well-defined boundaries? Are invariants maintained within aggregates? * **Repositories:** Are repositories used to abstract data access and persistence concerns from the domain? * **Domain Events:** Are domain events used to decouple different parts of the system and react to significant changes in the domain? * **Services:** Are domain services used to encapsulate complex business logic that doesn't naturally belong to an entity or value object? **Clean Architecture:** * **Dependency Inversion Principle (DIP):** Are high-level modules (e.g., domain) independent of low-level modules (e.g., infrastructure)? Are abstractions (interfaces) used to decouple layers? * **Separation of Concerns:** Is the code organized into distinct layers with clear responsibilities? * **Testability:** Is the code easily testable, with minimal dependencies on external systems? * **Framework Independence:** Is the core business logic independent of specific frameworks and technologies? * **UI Independence:** Is the core business logic independent of the user interface? * **Database Independence:** Is the core business logic independent of the database? * **Use Cases (Interactors):** Are use cases clearly defined and implemented as interactors that orchestrate the domain logic? * **Entities (Domain Layer):** The innermost layer containing the core business entities and rules. * **Application Layer:** Contains use cases and application services that orchestrate the domain logic. * **Interface Adapters Layer:** Adapts data between the application layer and external systems (e.g., UI, database). * **Infrastructure Layer:** Provides concrete implementations of interfaces defined in the inner layers (e.g., database access, external API calls). **My Analysis will include:** * **Identification of potential violations of DDD and Clean Architecture principles.** * **Suggestions for improvements, including code examples where appropriate.** * **Explanation of the reasoning behind my recommendations.** **Example:** You might provide me with this Go code: ```go package main import ( "database/sql" "fmt" _ "github.com/lib/pq" ) type User struct { ID int Name string Email string } func GetUser(db *sql.DB, id int) (User, error) { query := "SELECT id, name, email FROM users WHERE id = $1" row := db.QueryRow(query, id) var user User err := row.Scan(&user.ID, &user.Name, &user.Email) if err != nil { return User{}, err } return user, nil } func main() { db, err := sql.Open("postgres", "user=postgres password=password dbname=mydb sslmode=disable") if err != nil { panic(err) } defer db.Close() user, err := GetUser(db, 1) if err != nil { panic(err) } fmt.Println(user) } ``` And I might respond with: "This code has several issues from a DDD and Clean Architecture perspective: 1. **Tight Coupling to Infrastructure:** The `GetUser` function directly depends on the `sql.DB` object. This tightly couples the code to the database implementation, making it difficult to test and change. 2. **Lack of Abstraction:** There's no abstraction layer (e.g., a repository) between the domain and the database. 3. **Domain Model Anemia:** The `User` struct is a simple data structure with no behavior. It's likely just a data transfer object (DTO) and not a rich domain entity. 4. **No Use Case:** The `main` function directly calls `GetUser`. There's no clear use case defined. Here's a possible refactoring to address these issues: ```go package main import ( "context" "database/sql" "fmt" _ "github.com/lib/pq" ) // Domain Layer type User struct { ID int Name string Email string } // Repository Interface type UserRepository interface { Get(ctx context.Context, id int) (User, error) } // Infrastructure Layer type PostgresUserRepository struct { db *sql.DB } func NewPostgresUserRepository(db *sql.DB) UserRepository { return &PostgresUserRepository{db: db} } func (r *PostgresUserRepository) Get(ctx context.Context, id int) (User, error) { query := "SELECT id, name, email FROM users WHERE id = $1" row := r.db.QueryRowContext(ctx, query, id) var user User err := row.Scan(&user.ID, &user.Name, &user.Email) if err != nil { return User{}, err } return user, nil } // Application Layer (Use Case) type GetUserUseCase struct { repo UserRepository } func NewGetUserUseCase(repo UserRepository) *GetUserUseCase { return &GetUserUseCase{repo: repo} } func (uc *GetUserUseCase) Execute(ctx context.Context, id int) (User, error) { return uc.repo.Get(ctx, id) } func main() { db, err := sql.Open("postgres", "user=postgres password=password dbname=mydb sslmode=disable") if err != nil { panic(err) } defer db.Close() repo := NewPostgresUserRepository(db) useCase := NewGetUserUseCase(repo) user, err := useCase.Execute(context.Background(), 1) if err != nil { panic(err) } fmt.Println(user) } ``` This refactored code introduces a `UserRepository` interface to abstract data access, a `PostgresUserRepository` implementation in the infrastructure layer, and a `GetUserUseCase` to represent a specific use case. This makes the code more testable, maintainable, and aligned with DDD and Clean Architecture principles. Further improvements could include adding domain logic to the `User` entity and using domain events." Now, please provide me with the text or code you want me to work with!

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.

Asana MCP Server

An MCP (Multi-Agent Conversation Protocol) server that enables interacting with the Asana API through natural language commands for task management, project organization, and team collaboration.

DIE MCP Server

An MCP server that enables AI agents to analyze executable files using Detect It Easy (DIE), providing capabilities to examine file structures, detect packers, compilers, and gather other forensic information.

Steam MCP Server

Provides tools for interacting with the Steam Web API to access player profiles, game libraries, achievements, statistics, inventories, and game information through 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.

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.

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.

Cyber Sentinel MCP Server

A threat intelligence aggregation server that provides unified access to multiple security sources for analyzing indicators (IPs, domains, hashes, URLs) with confidence scoring.

Weather Alerts MCP Server

Enables users to fetch real-time weather alerts from the National Weather Service API for any US state. Provides formatted weather warnings, watches, and advisories with severity levels and safety instructions.

Confluence MCP Server

鏡 (Kagami)

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.

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.

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.