Discover Awesome MCP Servers

mcp-project-helper

Provides a minimal MCP server with safe tools for AI assistants to search project files, read files, search local documentation, and run pre-approved checks within a single project directory.

weppy-railway

MCP server for Roblox Studio integration, enabling AI agents to interact with Roblox through SSE transport and a local HTTP bridge for plugin communication.

capacitor-stack

MCP server for building React + Capacitor + Tailwind + Konsta applications, providing tools for UI snippets, plugin management, and stack recommendations.

veris

Provenance-first web access for AI agents, delivering clean content with verifiable source metadata and SEC EDGAR financial data.

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.

mcp-librivox

Enables searching and retrieving audiobooks, authors, and tracks from LibriVox through an MCP interface.

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.

agent-postit

A local MCP server that provides AI agents with persistent sticky-note memory, storing Markdown notes on disk and offering tools for creating, reading, updating, deleting, searching, and listing notes across sessions.

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.

ATLAS MCP

Safe, reliable, and verifiable execution for the Model Context Protocol, enabling governed tasks with planning, capability approval, side-effect verification, and evidence receipts.

우리어디가? MCP

Converts package tour schedules and travel information into card-style guides for easy sharing on KakaoTalk. Helps travelers reorganize scattered details from PDFs, chat messages, and websites into structured daily guides, packing lists, cost summaries, and emergency cards.

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.

micropub-mcp

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

Reevl MCP

Enables AI assistants to query Korean apartment real-estate data, including official transaction prices, jeonse ratios, and AI price forecasts for 45,000+ complexes.

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.

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 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.

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.

ae-mcp

Enables AI assistants to operate Adobe After Effects with the dexterity of an editor: reading projects, creating and rigging layers, setting real velocity curves, animating text, building native SVG-based shapes, applying effects, expressions, masks, and viewing render snapshots to verify results.

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!

oura-mcp

An MCP server that enables AI assistants to access Oura Ring health data including activity, sleep, heart rate, stress, and personal info.

mcp-housing-intel

Chains FRED, BLS, ATTOM, and HUD APIs to provide housing intelligence data through a single MCP server.

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.

daedalus

Enables driving a real Chrome instance remotely through MCP, including per-tab control, JavaScript evaluation, screenshots, cookie and network capture, and CDP fallback with token-based authentication.

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

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.

space-feeds

Enables AI agents to access curated space and spaceflight RSS/Atom feeds via tools to list feeds, read feed items, and fetch any feed by URL.

Rapid7 Bulk Export MCP Server

Enables AI-powered analysis of Rapid7 vulnerability and asset data by exporting via the Bulk Export API and making it queryable through natural language or SQL in MCP-compatible tools.