发现优秀的 MCP 服务器

deepseek-mcp-server

MCP server for DeepSeek AI models (Chat + Reasoner). Supports multi-turn sessions, model fallback with circuit breaker, function calling, thinking mode, JSON output, multimodal input, and cost tracking.

LLM_MCP

构建用于 LLM 的 MCP 客户端和服务器 (Gòu jiàn yòng yú LLM de MCP kèhùduān hé fúwùqì)

agentek-eth

一个提供加密货币研究和基于以太坊的自动化工具的 MCP 服务器。

stock-moves-explained

Explains why US stocks moved. AI analysis for 'why did Tesla drop?' queries. Covers S\&P 500, NASDAQ 100, Dow 30 (~550 stocks).

Discord MCP Server

An MCP server for interacting with Discord.

Universal DB MCP

Enables Claude Desktop to interact with MySQL, PostgreSQL, and Redis databases using natural language for data querying and schema analysis. It provides a secure interface with a default read-only mode to prevent unauthorized database modifications.

Kagi MCP Server (Node.js)

好的，这是将“Node implementation of a Kagi MCP server”翻译成中文的几种方式，根据不同的语境，选择最合适的： * **最直接的翻译：** Kagi MCP 服务器的 Node 实现 * **更自然的翻译：** 使用 Node 实现的 Kagi MCP 服务器 * **更详细的翻译：** 基于 Node.js 的 Kagi MCP 服务器实现 **解释：** * **Kagi MCP server:** Kagi MCP 服务器 (MCP 通常指 Message Control Protocol，消息控制协议，但具体含义需要根据 Kagi 的上下文来确定) * **Node implementation:** Node 实现 (指使用 Node.js 编程语言来实现) 因此，根据你希望表达的重点，可以选择以上任何一种翻译。 如果需要更精确的翻译，请提供更多关于 Kagi MCP server 的信息。

SuperColony

MCP server providing live access to the SuperColony agent swarm on Demos Network. 7 tools: read the agent feed (filtered by category/asset), search posts, get AI-synthesized consensus signals, view agent profiles with on-chain identities, check network stats, browse the quality leaderboard, and fetch the full integration guide for building your own agent.

Crossword MCP Server

Enables solving crossword puzzles by loading grid and clue files, registering candidate words, checking consistency, and rendering final solutions. Supports Japanese crosswords with automated solving capabilities through natural language interaction.

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!

MCP Database Server

This server enables Claude to directly interact with SQLite, SQL Server, PostgreSQL, and MySQL databases through the Model Context Protocol, allowing for query execution, table management, and data export capabilities.

Schwab Model Context Protocol Server

一个实现了施瓦布（Schwab）API 的模型上下文协议（Model Context Protocol）的服务器，允许访问账户信息、持仓、股票报价以及订单/交易历史，专为与大型语言模型集成而设计。

@tailor-platform/tailor-mcp

Tailorctl 命令行实用程序，重点关注 MCP（模型上下文协议）服务器功能。

🚀 MCP Gemini Search

使用 Gemini 2.5 Pro 的模型上下文协议 (MCP)。使用 Gemini 的函数调用功能和 MCP 的航班搜索工具将对话式查询转换为航班搜索。

MCP Chat

A server that enables users to chat with each other by repurposing the Model Context Protocol (MCP), designed for AI tool calls, into a human-to-human communication system.

Magento 2 MCP Server

A Model Context Protocol server that connects to a Magento 2 REST API, allowing Claude and other MCP clients to query product information, customer data, and order statistics from a Magento store.

MCP Brain Server

Provides AI agents with a learning-based knowledge management system that stores, retrieves, and automatically relates knowledge using embeddings, with Git-based version control and Obsidian compatibility.

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.

MCP VectorStore Server

Provides advanced document search and processing capabilities through vector stores, including PDF processing, semantic search, web search integration, and file operations. Enables users to create searchable document collections and retrieve relevant information using natural language queries.

MCP Proxy

一个为使用标准输入输出 (stdio) 传输的 MCP 服务器设计的 TypeScript SSE 代理。 (Simplified: 一个为使用 stdio 的 MCP 服务器设计的 TypeScript SSE 代理。)

Italian Cities MCP Server

Provides CRUD tools for managing a database of Italian cities backed by Elasticsearch. It enables AI assistants to search, create, update, and delete city records through a standardized Model Context Protocol interface.

Design Tokens - MCP Server

好的，这是翻译成中文： 一个用于 Figma 的 MCP 服务器。它也可以创建图像。 (Yī gè yòng yú Figma de MCP fúwùqì. Tā yě kěyǐ chuàngjiàn túxiàng.)

TypeScript MCP Sample Server

A sample MCP server implementation in TypeScript that demonstrates tool creation with a mock AI completion endpoint. Provides a basic example for developers to learn MCP server development patterns and tool registration.

CoinMarketCap MCP Server

从 CoinMarketCap API 实时访问加密货币数据。

mcp-qemu-lab

A Model Context Protocol server for conducting Linux binary analysis and guest system forensics within QEMU virtual machines. It enables automated VM lifecycle management, memory dumping, and interactive debugging workflows for analyzing processes and artifacts.

py-ue5-mcp-server

一个UE5 MCP服务器，允许用户访问蓝图Actor中的函数。 (Yī gè UE5 MCP fúwùqì, yǔnxǔ yònghù fǎngwèn lán tú Actor zhōng de hánshù.)

Carbon Voice

Access your Carbon Voice conversations and voice memos—retrieving messages, conversations, voice memos, and more as well as send messages to Carbon Voice users.

Ableton Copilot MCP

一个模型上下文协议服务器，能够与 Ableton Live 进行实时交互，从而使 AI 助手能够控制歌曲创作、音轨管理、片段操作和音频录制工作流程。