Discover Awesome MCP Servers

Perplexity MCP Server

Mirror of

mariadb-mcp-server

An mcp server that provides read-only access to MariaDB.

TimezoneToolkit MCP Server

Server MCP tingkat lanjut yang menyediakan alat waktu dan zona waktu yang komprehensif.

AWS Model Context Protocol (MCP) Server

Layanan ringan yang memungkinkan asisten AI untuk menjalankan perintah AWS CLI melalui Model Context Protocol (MCP), memungkinkan alat AI untuk mengambil dokumentasi AWS dan berinteraksi dengan layanan AWS.

MCP Command History

Sebuah alat yang ampuh untuk menjelajahi, mencari, dan mengelola riwayat perintah shell Anda melalui antarmuka MCP (Model Control Protocol). Proyek ini memungkinkan Anda untuk dengan mudah mengakses, mencari, dan mengambil perintah shell yang sebelumnya telah Anda jalankan.

Gitee MCP Server

Integrasi API Gitee, pengelolaan repositori, isu, dan *pull request*, dan lainnya.

Memory Bank MCP Server 2.2.1

Sebuah server untuk mengelola dokumentasi proyek dan konteks di seluruh sesi Claude AI melalui bank memori global dan khusus cabang, memungkinkan manajemen pengetahuan yang konsisten dengan penyimpanan dokumen JSON terstruktur.

MCP Server

MCP server implementation for handling run_python requests

MCP Server - Oracle DB Context

Here are a few ways you could translate "MCP Server for working with large Oracle databases" into Indonesian, depending on the specific nuance you want to convey: **Option 1 (Most straightforward):** * **Server MCP untuk bekerja dengan database Oracle berukuran besar.** * This is a direct translation and easily understood. **Option 2 (Emphasizing the purpose):** * **Server MCP untuk mengelola database Oracle berukuran besar.** * "Mengelola" translates to "manage" or "handle," which might be more appropriate if the server is used for more than just "working with" the database. **Option 3 (More technical, using "basis data"):** * **Server MCP untuk bekerja dengan basis data Oracle berukuran besar.** * "Basis data" is the more formal Indonesian term for "database." **Option 4 (Focusing on the scale):** * **Server MCP untuk menangani database Oracle skala besar.** * "Skala besar" emphasizes the large scale of the database. **Which one is best depends on the context. If you want a simple, clear translation, Option 1 is a good choice. If you want to emphasize management or the large scale, Options 2 or 4 might be better.**

MCP Chunk Editor

Sebuah server MCP yang menyediakan editor teks yang efisien dan aman untuk LLM.

kagi-server MCP Server

Mirror of

vigilant-adventure

hey,i wanted to play with some mods but when i try to open the game it says; The game crashed whilst initializing game Error: java.lang.NoClassDefFoundError: cpw/mods/fml/common/IPlayerTracker Exit Code: -1 what can i do to make it work?the log is; ---- Minecraft Crash Report ---- WARNING: coremods are present: MekanismCoremod (Mekanism-1.12.2-9…

Better Qdrant MCP Server

Sebuah server Protokol Konteks Model yang memungkinkan kemampuan pencarian semantik dengan menyediakan alat untuk mengelola koleksi basis data vektor Qdrant, memproses dan menyematkan dokumen menggunakan berbagai layanan penyematan, dan melakukan pencarian semantik di seluruh penyematan vektor.

.NET MCP Servers

Collection of my MCP (Model Context Protocol) servers written in .NET

Figma to Vue MCP Server

MCP server that generates Vue components from Figma designs following Hostinger's design system

MCP Image Generation Server

Mirror of

WCGW

Send code snippet and paths to Claude. Designed to work with wcgw mcp server.

artifacts-mcp

MCP Server for Artifacts MMO

Knowledge Graph Memory Server

Cermin dari

mem0 MCP Server

Implementasi TypeScript dari server Protokol Konteks Model yang memungkinkan pembuatan, pengelolaan, dan pencarian semantik aliran memori dengan integrasi Mem0.

mcp-server-bluesky

Mirror of

mcp-server-web3

The web3 function plugin server base on MCP of Anthropic.

Prompt Decorators

A standardized framework for enhancing how LLMs process and respond to prompts through composable decorators, featuring an official open standard specification and Python reference implementation with MCP server integration.

McpDocs

Provide documentation about your Elixir project's functions and functions of dependencies to an LLM through an SSE MCP server.

mpc-csharp-semantickernel

Okay, here's an example of how you might use Microsoft Semantic Kernel with OpenAI and an MCP (Management Control Plane) Server. This example is conceptual and assumes you have an MCP server set up to manage your OpenAI API keys and rate limits. It focuses on the core ideas and provides a starting point. **Conceptual Scenario:** Imagine you're building a chatbot that helps users write marketing copy. You want to use OpenAI's GPT models for generating the copy, but you also want to: * **Manage OpenAI API Keys Centrally:** You don't want to hardcode API keys in your application. The MCP server will handle key rotation and access control. * **Enforce Rate Limits:** You want to prevent abuse and stay within your OpenAI usage limits. The MCP server will track and enforce these limits. * **Monitor Usage:** You want to track how much your application is using OpenAI to optimize costs and performance. The MCP server will provide metrics. **Code Example (C#):** ```csharp using Microsoft.SemanticKernel; using Microsoft.SemanticKernel.Connectors.OpenAI; using System.Net.Http; using System.Text.Json; using System.Threading.Tasks; public class MarketingCopyBot { private readonly IKernel _kernel; private readonly HttpClient _httpClient; private readonly string _mcpServerUrl; public MarketingCopyBot(string mcpServerUrl) { _mcpServerUrl = mcpServerUrl; _httpClient = new HttpClient(); _kernel = new KernelBuilder().Build(); } private async Task<string> GetOpenAIKeyFromMCP() { // Replace with your MCP server endpoint for retrieving OpenAI keys. string endpoint = $"{_mcpServerUrl}/api/openai/key"; try { HttpResponseMessage response = await _httpClient.GetAsync(endpoint); response.EnsureSuccessStatusCode(); // Throw exception if not successful string jsonResponse = await response.Content.ReadAsStringAsync(); // Assuming the MCP server returns a JSON object like: // { "apiKey": "YOUR_OPENAI_API_KEY" } using (JsonDocument document = JsonDocument.Parse(jsonResponse)) { JsonElement root = document.RootElement; if (root.TryGetProperty("apiKey", out JsonElement apiKeyElement)) { return apiKeyElement.GetString(); } else { Console.WriteLine($"Error: 'apiKey' property not found in MCP response: {jsonResponse}"); return null; } } } catch (HttpRequestException ex) { Console.WriteLine($"Error communicating with MCP server: {ex.Message}"); return null; } catch (JsonException ex) { Console.WriteLine($"Error parsing JSON from MCP server: {ex.Message}"); return null; } } public async Task InitializeKernel() { string apiKey = await GetOpenAIKeyFromMCP(); if (string.IsNullOrEmpty(apiKey)) { Console.WriteLine("Failed to retrieve OpenAI API key from MCP. Exiting."); return; // Or throw an exception } // Configure OpenAI connector using the key retrieved from MCP _kernel.Config.AddOpenAICompletionService( "text-davinci-003", // Or your preferred model apiKey ); } public async Task<string> GenerateMarketingCopy(string productDescription, string targetAudience) { // Check if the kernel is initialized if (_kernel.Config.GetService<ICompletionService>("text-davinci-003") == null) { Console.WriteLine("Kernel not initialized. Call InitializeKernel() first."); return "Error: Kernel not initialized."; } // Define the prompt using Semantic Kernel's templating string promptTemplate = @" Write compelling marketing copy for a product described as: {{productDescription}}. The target audience is: {{targetAudience}}. "; // Create a semantic function var generateCopyFunction = _kernel.CreateSemanticFunction(promptTemplate); // Execute the function var arguments = new KernelArguments { ["productDescription"] = productDescription, ["targetAudience"] = targetAudience }; try { var result = await generateCopyFunction.InvokeAsync(arguments); return result.ToString(); } catch (Exception ex) { Console.WriteLine($"Error generating marketing copy: {ex.Message}"); return $"Error: {ex.Message}"; } } public static async Task Main(string[] args) { // Replace with the actual URL of your MCP server string mcpServerUrl = "http://your-mcp-server:8080"; var bot = new MarketingCopyBot(mcpServerUrl); await bot.InitializeKernel(); if (_kernel.Config.GetService<ICompletionService>("text-davinci-003") != null) { string productDescription = "A revolutionary new noise-canceling headphone."; string targetAudience = "Young professionals who work in open office environments."; string marketingCopy = await bot.GenerateMarketingCopy(productDescription, targetAudience); Console.WriteLine(marketingCopy); } else { Console.WriteLine("Bot initialization failed. Check MCP server connection and API key retrieval."); } } } ``` **Explanation:** 1. **MCP Server Interaction:** * The `GetOpenAIKeyFromMCP()` method is responsible for communicating with your MCP server. It makes an HTTP request to a specific endpoint (e.g., `/api/openai/key`). * It parses the JSON response from the MCP server to extract the OpenAI API key. **Important:** The format of the JSON response will depend on how your MCP server is designed. * Error handling is included to catch potential issues with network connectivity or JSON parsing. 2. **Kernel Initialization:** * The `InitializeKernel()` method retrieves the API key from the MCP server. * It then uses the `_kernel.Config.AddOpenAICompletionService()` method to configure the Semantic Kernel to use OpenAI, providing the retrieved API key. This is where the connection to OpenAI is established. 3. **Semantic Function:** * The `GenerateMarketingCopy()` method defines a semantic function using Semantic Kernel's templating language. The template includes placeholders for the product description and target audience. * It creates a `KernelArguments` object to pass the actual values for these placeholders. * It executes the semantic function using `generateCopyFunction.InvokeAsync()`. 4. **Error Handling:** * The code includes `try-catch` blocks to handle potential exceptions during API key retrieval and semantic function execution. 5. **Main Method:** * The `Main` method creates an instance of the `MarketingCopyBot`, initializes the kernel, and then calls the `GenerateMarketingCopy()` method to generate marketing copy. * It prints the generated copy to the console. **Key Considerations and Indonesian Translation:** * **MCP Server Implementation:** This example assumes you have an MCP server already set up. The specific implementation of the MCP server is beyond the scope of this example. You'll need to design and implement the MCP server to handle API key management, rate limiting, and usage monitoring. * **Terjemahan:** Contoh ini mengasumsikan Anda sudah memiliki server MCP yang disiapkan. Implementasi spesifik server MCP berada di luar cakupan contoh ini. Anda perlu merancang dan mengimplementasikan server MCP untuk menangani manajemen kunci API, pembatasan laju, dan pemantauan penggunaan. * **MCP Server Endpoint:** The `/api/openai/key` endpoint is just an example. You'll need to define the actual endpoint based on your MCP server's API. * **Terjemahan:** Endpoint `/api/openai/key` hanyalah contoh. Anda perlu menentukan endpoint sebenarnya berdasarkan API server MCP Anda. * **Authentication/Authorization:** The example doesn't include any authentication or authorization when communicating with the MCP server. In a real-world scenario, you'll need to add appropriate security measures to protect your API keys. This might involve using API keys, OAuth, or other authentication mechanisms. * **Terjemahan:** Contoh ini tidak menyertakan otentikasi atau otorisasi apa pun saat berkomunikasi dengan server MCP. Dalam skenario dunia nyata, Anda perlu menambahkan langkah-langkah keamanan yang sesuai untuk melindungi kunci API Anda. Ini mungkin melibatkan penggunaan kunci API, OAuth, atau mekanisme otentikasi lainnya. * **Rate Limiting:** The example doesn't explicitly implement rate limiting. You'll need to integrate with your MCP server's rate limiting features. This might involve checking the number of requests made within a certain time period and delaying requests if necessary. The MCP server should ideally return information about rate limits in its responses. * **Terjemahan:** Contoh ini tidak secara eksplisit mengimplementasikan pembatasan laju. Anda perlu berintegrasi dengan fitur pembatasan laju server MCP Anda. Ini mungkin melibatkan pemeriksaan jumlah permintaan yang dibuat dalam jangka waktu tertentu dan menunda permintaan jika perlu. Idealnya, server MCP harus mengembalikan informasi tentang batasan laju dalam responsnya. * **Error Handling:** The error handling in the example is basic. You should implement more robust error handling to gracefully handle different types of errors and provide informative messages to the user. * **Terjemahan:** Penanganan kesalahan dalam contoh ini bersifat dasar. Anda harus mengimplementasikan penanganan kesalahan yang lebih kuat untuk menangani berbagai jenis kesalahan dengan baik dan memberikan pesan yang informatif kepada pengguna. * **Dependency Injection:** Consider using dependency injection to make your code more testable and maintainable. * **Terjemahan:** Pertimbangkan untuk menggunakan injeksi dependensi untuk membuat kode Anda lebih mudah diuji dan dipelihara. * **Asynchronous Operations:** The example uses `async` and `await` for asynchronous operations, which is good practice for I/O-bound tasks like network requests. * **Terjemahan:** Contoh ini menggunakan `async` dan `await` untuk operasi asinkron, yang merupakan praktik yang baik untuk tugas-tugas terikat I/O seperti permintaan jaringan. **Indonesian Summary:** Contoh kode di atas menunjukkan cara menggunakan Microsoft Semantic Kernel dengan OpenAI dan server MCP. Server MCP digunakan untuk mengelola kunci API OpenAI, menerapkan batasan laju, dan memantau penggunaan. Kode mengambil kunci API dari server MCP, menginisialisasi Semantic Kernel dengan kunci tersebut, dan kemudian menggunakan Semantic Kernel untuk menghasilkan teks pemasaran menggunakan model OpenAI. Penting untuk dicatat bahwa contoh ini bersifat konseptual dan memerlukan implementasi server MCP yang sesuai. Selain itu, perlu ditambahkan langkah-langkah keamanan, penanganan kesalahan yang lebih kuat, dan integrasi dengan fitur pembatasan laju server MCP. This example provides a solid foundation for building applications that leverage OpenAI while maintaining control over API keys and usage. Remember to adapt the code to your specific MCP server implementation and security requirements. Good luck!

Linear MCP Server

Mirror of