Discover Awesome MCP Servers

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.

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 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!

GeoRanker MCP Server

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

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.

market-recap

Provides a one-call snapshot of recent market activity and events.

Rideshare Comparison MCP

Compare Uber and Lyft prices for any route, get supported cities, and retrieve booking links using Claude Desktop.

Jira Data Center MCP Server

Enables LLMs to interact with Atlassian Jira Data Center through natural language queries for semantic search and automated workflow execution. It provides secure tools to discover, inspect, and execute Jira API operations using production-ready authentication methods.

mcp-searxng-tool

AIエージェントがSearXNGサービスを通じて外部ウェブサイトのコンテンツや情報を検索できるようにするためのMCPサーバー。

cursor-agent-bridge

Enables MCP clients like Claude Code to delegate coding tasks to the local Cursor Agent CLI, with persistent per-workspace sessions that resume across calls.

pendpost

Pendpost MCP server — exposes ~43 tools for posting, scheduling, and managing content via the Pendpost platform.

onbid-mcp

Enables LLM clients to ask plain-language questions about Korean public auction property data from the 온비드 OpenAPI, including normalized pricing, location, and failed-sale history.

Figbridge

Bridges Figma and code for AI agents, enabling reading design data, importing live URLs into Figma, auditing designs, and generating source patches via 48 MCP tools.

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.

@ikenga/mcp-iyke

MCP server that exposes the Ikenga desktop app's iyke control bridge, allowing MCP clients to drive a running Ikenga session via tools for navigation, runtime inspection, and project management.

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.

ShortsMonkey MCP

A read-only MCP server for discovering YouTube outliers, analyzing viral Shorts, and evaluating single-video performance using stored snapshots.

Sentry MCP Server

Enables comprehensive Sentry monitoring including performance analysis, issue tracking, and transaction tracing.

Income Screener MCP Server

Connects Claude to E\*Trade for income-oriented trading research, providing real-time quotes, option chain lookups, and put-sell candidate screening with annualized yield filters.

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.

research-mcp

A stateless MCP server that provides web search, single page reading, and batch page reading tools, with automatic failover across multiple search and content extraction providers.

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.

debugbase-mcp

Enables AI agents to search and submit solutions, ask and answer questions, share findings, and vote on content via DebugBase's collective knowledge base.

TechWord Translator MCP Server

Provides translation services for technical terms across English, Spanish, and German via MCP tools for search and translation.

MCP Fish Server

Enables text-to-speech generation via Fish Audio models, including voice library browsing and task management.

Frappe Assistant Core

MCP server that enables LLMs to interact with ERPNext/Frappe sites for document CRUD, search, reports, workflows, and analytics, respecting user permissions and logging all actions.

hwatu

Verification browser for coding agents. Headless-by-default WebKit windows with DOM eval, screenshots, pixel-diff with a real match percentage and heatmap, and live hand-off to a human. One static binary, no Chromium.

Yandex Direct MCP Server

Integrates with Yandex Direct API v5 to manage ads via 20 tools, with dry-run protection preventing accidental spending.

NewsMCP — World news for AI agents

Real-time news events, clustered by AI from hundreds of sources, classified by topic and geography, ranked by importance.