Discover Awesome MCP Servers

@leonardocrdso/clean-code-mcp

An MCP server that provides 63 Clean Code principles for AI-assisted code review and development.

material-icons-agent-skill

Enables AI agents to search, import, migrate, validate, and maintain official Google Material Symbols across various platforms and IDEs.

ClickUp MCP Server

Enables comprehensive project management through ClickUp's API, supporting task creation and updates, time tracking, goal management, and team collaboration within ClickUp's hierarchical workspace structure.

Dooray MCP Server

Enables integration with Dooray project management platform, allowing users to manage projects, tasks, comments, milestones, tags, and team members through natural language interactions.

outlook-mcp

MCP server for Microsoft Outlook via Graph API. 20 consolidated tools for email, calendar, contacts, folders, rules, categories, and settings with safety controls (dry-run preview, rate limiting, recipient allowlists) and MCP annotations on every tool.

Claude Rules MCP Server

Serves 484 Claude Code skills and 17 global rules as MCP tools, enabling skill and rule retrieval, keyword search, and project-level rule discovery for any Claude client.

DeepSeek MCP Server

Okay, here's a basic outline and code snippet for a simple MCP (presumably meaning something like "Message Control Protocol" or "Message Coordination Proxy" in this context) server in Go that redirects questions to Deepseek models. I'll focus on the core functionality: receiving a request, forwarding it to a Deepseek model (assuming a Deepseek API endpoint), and returning the response. **Important Considerations and Assumptions:** * **Deepseek API:** This code assumes you have access to a Deepseek API endpoint that accepts a question (likely as a JSON payload) and returns a response (also likely as JSON). You'll need to replace the placeholder URL (`"https://api.deepseek.com/v1/completions"`) with the actual Deepseek API endpoint. You'll also need to handle authentication (API keys, etc.) as required by the Deepseek API. * **Error Handling:** The code includes basic error handling, but you'll want to expand it for production use. Consider logging errors, implementing retry mechanisms, and providing more informative error responses to the client. * **JSON Handling:** The code uses JSON for request and response serialization. Adjust the data structures if the Deepseek API uses a different format. * **Concurrency:** The code handles each request in a separate goroutine, allowing the server to handle multiple requests concurrently. * **Dependencies:** You'll need the `net/http` and `encoding/json` packages, which are part of the Go standard library. You might also need a package like `io/ioutil` for reading the response body. * **Security:** This is a *very* basic example. For production, you'll need to consider security aspects like input validation, rate limiting, and authentication/authorization. * **MCP Definition:** I'm assuming "MCP" is a custom term in your context. This code provides a simple proxy/redirector. If MCP has more specific requirements (e.g., message queuing, transformation, routing rules), you'll need to adapt the code accordingly. **Code Example (main.go):** ```go package main import ( "bytes" "encoding/json" "fmt" "io" "log" "net/http" "os" ) // RequestPayload represents the structure of the incoming request. Adjust as needed. type RequestPayload struct { Question string `json:"question"` } // ResponsePayload represents the structure of the response from Deepseek. Adjust as needed. type ResponsePayload struct { Answer string `json:"answer"` } // deepseekAPIEndpoint is a placeholder. Replace with the actual Deepseek API URL. const deepseekAPIEndpoint = "https://api.deepseek.com/v1/completions" // Replace with the actual Deepseek API endpoint // deepseekAPIKey is a placeholder. Replace with your actual Deepseek API key. const deepseekAPIKey = "YOUR_DEEPSEEK_API_KEY" // Replace with your actual Deepseek API key func main() { http.HandleFunc("/", handleRequest) port := os.Getenv("PORT") if port == "" { port = "8080" // Default port } fmt.Printf("Server listening on port %s\n", port) log.Fatal(http.ListenAndServe(":"+port, nil)) } func handleRequest(w http.ResponseWriter, r *http.Request) { if r.Method != http.MethodPost { http.Error(w, "Method not allowed", http.StatusMethodNotAllowed) return } // Decode the incoming JSON request var requestPayload RequestPayload err := json.NewDecoder(r.Body).Decode(&requestPayload) if err != nil { http.Error(w, "Invalid request body: "+err.Error(), http.StatusBadRequest) return } // Forward the request to the Deepseek API deepseekResponse, err := forwardToDeepseek(requestPayload) if err != nil { http.Error(w, "Error forwarding to Deepseek: "+err.Error(), http.StatusInternalServerError) return } // Encode the Deepseek response as JSON and send it back to the client w.Header().Set("Content-Type", "application/json") err = json.NewEncoder(w).Encode(deepseekResponse) if err != nil { http.Error(w, "Error encoding response: "+err.Error(), http.StatusInternalServerError) return } } func forwardToDeepseek(requestPayload RequestPayload) (ResponsePayload, error) { // Prepare the request to Deepseek requestBody, err := json.Marshal(map[string]string{"prompt": requestPayload.Question}) // Adjust the payload as needed for Deepseek if err != nil { return ResponsePayload{}, fmt.Errorf("error marshaling request to Deepseek: %w", err) } req, err := http.NewRequest(http.MethodPost, deepseekAPIEndpoint, bytes.NewBuffer(requestBody)) if err != nil { return ResponsePayload{}, fmt.Errorf("error creating Deepseek request: %w", err) } req.Header.Set("Content-Type", "application/json") req.Header.Set("Authorization", "Bearer "+deepseekAPIKey) // Add API key if required // Send the request to Deepseek client := &http.Client{} resp, err := client.Do(req) if err != nil { return ResponsePayload{}, fmt.Errorf("error sending request to Deepseek: %w", err) } defer resp.Body.Close() // Read the response from Deepseek body, err := io.ReadAll(resp.Body) if err != nil { return ResponsePayload{}, fmt.Errorf("error reading Deepseek response: %w", err) } if resp.StatusCode != http.StatusOK { return ResponsePayload{}, fmt.Errorf("Deepseek API returned error: %s, status code: %d", string(body), resp.StatusCode) } // Decode the Deepseek response var deepseekResponse ResponsePayload err = json.Unmarshal(body, &deepseekResponse) if err != nil { return ResponsePayload{}, fmt.Errorf("error unmarshaling Deepseek response: %w", err) } return deepseekResponse, nil } ``` **How to Run:** 1. **Save:** Save the code as `main.go`. 2. **Dependencies:** Make sure you have Go installed. You don't need to install any external dependencies for this example, as it uses only the standard library. 3. **Replace Placeholders:** **Crucially, replace `"https://api.deepseek.com/v1/completions"` and `"YOUR_DEEPSEEK_API_KEY"` with the actual Deepseek API endpoint and your API key.** 4. **Run:** Open a terminal, navigate to the directory where you saved `main.go`, and run `go run main.go`. 5. **Test:** Use a tool like `curl` or Postman to send a POST request to `http://localhost:8080/` (or the port you configured). The request body should be JSON like this: ```json { "question": "What is the capital of France?" } ``` **Example `curl` command:** ```bash curl -X POST -H "Content-Type: application/json" -d '{"question": "What is the capital of France?"}' http://localhost:8080/ ``` **Explanation:** 1. **`main` Function:** * Sets up an HTTP handler that listens for requests on the root path (`/`). * Starts the HTTP server on port 8080 (or the port specified by the `PORT` environment variable). 2. **`handleRequest` Function:** * Checks if the request method is POST. * Decodes the JSON request body into a `RequestPayload` struct. * Calls `forwardToDeepseek` to send the question to the Deepseek API. * Encodes the Deepseek response as JSON and sends it back to the client. * Handles errors appropriately. 3. **`forwardToDeepseek` Function:** * Marshals the `RequestPayload` into a JSON payload suitable for the Deepseek API. **You'll need to adjust the structure of this payload to match the Deepseek API's requirements.** * Creates an HTTP request to the Deepseek API endpoint. * Sets the `Content-Type` header to `application/json`. * **Adds the Deepseek API key to the `Authorization` header (if required).** * Sends the request to the Deepseek API. * Reads the response from the Deepseek API. * Decodes the JSON response into a `ResponsePayload` struct. * Handles errors appropriately. **Chinese Translation of Key Concepts:** * **MCP Server:** MCP 服务器 (MCP fúwùqì) * **Redirect:** 重定向 (chóngdìngxiàng) * **Deepseek Model:** Deepseek 模型 (Deepseek móxíng) * **API Endpoint:** API 端点 (API duāndiǎn) * **JSON:** JSON (pronounced the same in Chinese) * **Request:** 请求 (qǐngqiú) * **Response:** 响应 (xiǎngyìng) * **Error Handling:** 错误处理 (cuòwù chǔlǐ) * **Concurrency:** 并发 (bìngfā) * **Authentication:** 身份验证 (shēnfèn yànzhèng) * **Authorization:** 授权 (shòuquán) * **Payload:** 负载 (fùzài) * **Goroutine:** Goroutine (pronounced the same in Chinese) **Further Improvements:** * **Configuration:** Use environment variables or a configuration file to store the Deepseek API endpoint, API key, and other settings. * **Logging:** Implement more robust logging using a library like `logrus` or `zap`. * **Metrics:** Add metrics to track the number of requests, response times, and error rates. * **Rate Limiting:** Implement rate limiting to prevent abuse of the Deepseek API. * **Caching:** Cache responses from the Deepseek API to improve performance and reduce costs. * **Health Checks:** Add a health check endpoint to monitor the server's status. * **Load Balancing:** If you need to handle a large volume of traffic, consider using a load balancer to distribute requests across multiple instances of the server. * **Input Validation:** Thoroughly validate the incoming `question` to prevent injection attacks or other security vulnerabilities. Remember to adapt the code to the specific requirements of the Deepseek API you are using. Good luck!

🚀 Electron Debug MCP Server

🚀 一个强大的 MCP 服务器，用于调试 Electron 应用程序，并深度集成了 Chrome DevTools 协议。 通过标准化的 API 控制、监控和调试 Electron 应用程序。

Forgejo MCP Server

A Model Context Protocol (MCP) server that provides Claude Desktop with tools to interact with Forgejo repositories, including listing repos, issues, and reading files.

Word of the Day MCP Server

Provides comprehensive word definitions, pronunciations, meanings, and examples for any English word using the Free Dictionary API. Includes a random word-of-the-day feature with difficulty levels for vocabulary building.

MoA MCP Server

Enables users to leverage a mixture-of-agents architecture where multiple AI models discuss a question in parallel and a president model synthesizes the best answer.

Demo

A Python MCP server for Cline integration, providing tools for file analysis and other tasks. Supports switching to Google Gemini API.

Gemini Embedding 2 MCP Server

A multimodal local memory MCP server that lets AI agents search across text, PDFs, images, audio, and video using Gemini Embedding 2 and a local ChromaDB.

Apollo Proxy MCP Server

Provides AI agents with access to a global residential proxy network covering over 190 countries for web fetching and scraping. It enables pay-per-request transactions using USDC on the Base network via the x402 protocol.

Mnemo MCP

Persistent AI memory server with hybrid search and embedded sync. Enables AI agents to store, retrieve, and manage information across sessions with temporal knowledge graph support.

ci-triage-mcp

Exposes a Ci-mvp security-triage pipeline as chat tools for Claude Desktop, enabling on-demand queries of Trivy/Semgrep scan results stored in S3.

Gemini Flash Image MCP Server

Enables text-to-image generation, image editing, and multi-image composition using Google's Gemini 2.5 Flash Image API. Supports flexible aspect ratios and character consistency across generations.

MiniMax Unified MCP

Unifies MiniMax's multimodal generation, web search, image understanding, audio, video, and music tools into a single MCP server for use with Claude and other clients.

Remote MCP Server on Cloudflare

Enables deployment of a remote MCP server on Cloudflare Workers without authentication, allowing custom tools and integration with clients like Claude Desktop.

Asana MCP Server Extended

A Model Context Protocol (MCP) server that provides comprehensive Asana integration, enabling AI assistants like Claude to interact with Asana workspaces, projects, tasks, goals, portfolios, and more.

MCP MySQL Server

Enables Claude Desktop to interact with MySQL databases through secure query execution, schema discovery, and multi-database support with configurable read/write permissions and built-in SQL injection protection.

googlenews-mcp

Enables AI agents to read Google News without internet access, providing tools for top headlines, topic search, and news searches.

autocad-mcp

Enables AI agents to automate AutoCAD LT and create DXF files headless, with tools for drawing, entity, layer, block, annotation, PID, and system operations.

hut-reservation-mcp

Enables searching SAC huts, checking availability, and preparing booking or cancellation handoffs on hut-reservation.org with a safety model for writes.

DeepClaude MCP Server

This server integrates DeepSeek and Claude AI models to provide enhanced AI responses, featuring a RESTful API, configurable parameters, and robust error handling.

Remote MCP Server on Cloudflare

镜子 (jìng zi)

Node Terminal MCP

Enables AI agents to interact with terminal environments through multiple concurrent PTY sessions. Supports cross-platform terminal operations including command execution, session management, and real-time communication.

MCP ComfyUI Flux

Enables AI image generation using FLUX models through ComfyUI with GPU acceleration, supporting image generation, 4x upscaling, and background removal with optimized Docker deployment.

Codex SSH Terminal MCP

Agent-native SSH control plane with a local Web Terminal, human-in-the-loop secret input, keychain-backed profiles, and user-confirmed uploads for Codex, Claude Code, and MCP-compatible coding agents.

brand-gen

A memory-backed brand generation runtime for agent-led creative iteration. Enables AI agents to plan, generate, review, and improve brand materials with persistent brand memory and structured workflows.