Discover Awesome MCP Servers

Yahoo Finance MCP Server

Ini adalah server Protokol Konteks Model (MCP) yang menyediakan data keuangan komprehensif dari Yahoo Finance. Ini memungkinkan Anda untuk mengambil informasi rinci tentang saham, termasuk harga historis, informasi perusahaan, laporan keuangan, data opsi, dan berita pasar.

Todoist MCP Server

A Model Context Protocol server that enables advanced task and project management in Todoist via Claude Desktop and other MCP-compatible clients.

Sample Model Context Protocol Demos

Okay, here are some examples of how to use the Model Context Protocol with AWS, translated into Indonesian: **Judul: Kumpulan Contoh Penggunaan Protokol Konteks Model dengan AWS** **Pendahuluan:** Protokol Konteks Model (Model Context Protocol) adalah cara untuk menyediakan informasi kontekstual ke model machine learning Anda saat di-deploy. Informasi ini dapat mencakup data konfigurasi, kredensial, atau metadata lainnya yang dibutuhkan model untuk beroperasi dengan benar. Dengan AWS, Anda dapat memanfaatkan berbagai layanan untuk mengelola dan menyediakan konteks model ini. **Contoh 1: Menyediakan Kredensial AWS ke Model yang Berjalan di Amazon SageMaker** * **Bahasa Inggris:** "Let's say you have a model deployed on Amazon SageMaker that needs to access data from an S3 bucket. Instead of hardcoding the AWS credentials into the model code, you can use the SageMaker execution role to provide the necessary permissions. The model can then use the AWS SDK to assume the role and access the S3 bucket." * **Bahasa Indonesia:** "Katakanlah Anda memiliki model yang di-deploy di Amazon SageMaker yang perlu mengakses data dari bucket S3. Alih-alih memasukkan kredensial AWS secara langsung (hardcoding) ke dalam kode model, Anda dapat menggunakan peran eksekusi SageMaker untuk menyediakan izin yang diperlukan. Model kemudian dapat menggunakan AWS SDK untuk mengambil peran tersebut dan mengakses bucket S3." **Penjelasan:** * **SageMaker Execution Role:** Peran IAM yang diberikan ke instance SageMaker. Ini memberikan izin kepada instance untuk mengakses layanan AWS lainnya. * **AWS SDK:** Perpustakaan (library) yang memungkinkan model Anda berinteraksi dengan layanan AWS. * **Keuntungan:** Keamanan yang lebih baik (tidak ada kredensial yang di-hardcode), manajemen kredensial yang terpusat. **Contoh 2: Menggunakan AWS Secrets Manager untuk Menyimpan dan Mengakses Kunci API** * **Bahasa Inggris:** "Your model might need to call an external API that requires an API key. You can store the API key securely in AWS Secrets Manager and then retrieve it from your model at runtime. This prevents the API key from being exposed in your code or configuration files." * **Bahasa Indonesia:** "Model Anda mungkin perlu memanggil API eksternal yang memerlukan kunci API. Anda dapat menyimpan kunci API dengan aman di AWS Secrets Manager dan kemudian mengambilnya dari model Anda saat runtime. Ini mencegah kunci API terekspos dalam kode atau file konfigurasi Anda." **Penjelasan:** * **AWS Secrets Manager:** Layanan untuk menyimpan dan mengelola rahasia (secrets) seperti kunci API, kata sandi database, dan sertifikat. * **Runtime:** Waktu ketika model sedang berjalan dan memproses data. * **Keuntungan:** Keamanan yang ditingkatkan, rotasi rahasia yang mudah. **Contoh 3: Menggunakan AWS Systems Manager Parameter Store untuk Menyimpan Konfigurasi Model** * **Bahasa Inggris:** "You can use AWS Systems Manager Parameter Store to store configuration parameters for your model, such as the learning rate, batch size, or the path to a pre-trained model. This allows you to easily update the configuration without redeploying the model." * **Bahasa Indonesia:** "Anda dapat menggunakan AWS Systems Manager Parameter Store untuk menyimpan parameter konfigurasi untuk model Anda, seperti learning rate, ukuran batch, atau path ke model yang sudah dilatih sebelumnya (pre-trained model). Ini memungkinkan Anda untuk dengan mudah memperbarui konfigurasi tanpa perlu melakukan redeploy model." **Penjelasan:** * **AWS Systems Manager Parameter Store:** Layanan untuk menyimpan data konfigurasi dan rahasia. * **Learning Rate, Batch Size:** Contoh parameter yang sering digunakan dalam machine learning. * **Keuntungan:** Manajemen konfigurasi yang terpusat, pembaruan konfigurasi yang mudah. **Contoh 4: Menggunakan Amazon DynamoDB untuk Menyimpan Metadata Model** * **Bahasa Inggris:** "You can store metadata about your model in Amazon DynamoDB, such as the model version, training data used, and performance metrics. This metadata can be used for model tracking, auditing, and debugging." * **Bahasa Indonesia:** "Anda dapat menyimpan metadata tentang model Anda di Amazon DynamoDB, seperti versi model, data pelatihan yang digunakan, dan metrik kinerja. Metadata ini dapat digunakan untuk pelacakan model, audit, dan debugging." **Penjelasan:** * **Amazon DynamoDB:** Database NoSQL yang cepat dan scalable. * **Metadata:** Data tentang data (dalam hal ini, data tentang model). * **Keuntungan:** Pelacakan model yang lebih baik, kemampuan audit, dan debugging yang lebih mudah. **Contoh 5: Menggunakan AWS Lambda untuk Menyediakan Konteks Model Dinamis** * **Bahasa Inggris:** "You can use AWS Lambda to create a function that dynamically retrieves context information for your model based on the input data. For example, the Lambda function could retrieve user-specific data from a database and pass it to the model as context." * **Bahasa Indonesia:** "Anda dapat menggunakan AWS Lambda untuk membuat fungsi yang secara dinamis mengambil informasi konteks untuk model Anda berdasarkan data input. Misalnya, fungsi Lambda dapat mengambil data spesifik pengguna dari database dan meneruskannya ke model sebagai konteks." **Penjelasan:** * **AWS Lambda:** Layanan komputasi tanpa server (serverless) yang memungkinkan Anda menjalankan kode tanpa menyediakan atau mengelola server. * **Konteks Dinamis:** Informasi konteks yang berubah berdasarkan input. * **Keuntungan:** Fleksibilitas yang tinggi, kemampuan untuk menyediakan konteks yang dipersonalisasi. **Kesimpulan:** Contoh-contoh di atas menunjukkan beberapa cara untuk menggunakan Protokol Konteks Model dengan AWS. Dengan memanfaatkan layanan AWS seperti SageMaker, Secrets Manager, Parameter Store, DynamoDB, dan Lambda, Anda dapat mengelola dan menyediakan konteks model dengan aman dan efisien. Pilihan layanan yang tepat akan bergantung pada kebutuhan spesifik model dan aplikasi Anda. **Catatan:** Pastikan untuk selalu mengikuti praktik terbaik keamanan AWS saat mengelola kredensial dan data sensitif.

Bitwig MCP Server

MCP Server untuk Bitwig Studio

AverbePorto-MCP

AverbePorto MCP Server

Nmap-MCP

An agent-based network scanning system that uses Nmap for network discovery and leverages DeepSeek API to analyze scan results for security vulnerabilities and recommendations.

Linear Remote MCP server

Server Protokol Konteks Model Jarak Jauh (MCP) untuk Linear.

Nestjs Mcp

Server MCP terintegrasi untuk aplikasi NestJS Anda

Dune Analytics MCP Server

A Model Context Protocol server that connects AI agents to Dune Analytics data, providing access to DEX metrics, EigenLayer statistics, and Solana token balances through structured tools.

insights-mcp-server

Here are a few possible translations, depending on the context: * **Red Hat Insights MCP Server POC:** This is the most direct translation and likely the best if the audience is familiar with the acronyms and technical terms. * **POC Server MCP Red Hat Insights:** (Less common, but possible if emphasizing the "Proof of Concept" aspect) * **Proof of Concept (POC) Server MCP Red Hat Insights:** (More explicit, spelling out "Proof of Concept") **Explanation of Choices:** * **POC:** "Proof of Concept" is often used directly in Indonesian technical contexts, or abbreviated as "POC." * **MCP Server:** "MCP Server" is likely best left as is, unless you know what "MCP" stands for and can translate that appropriately. * **Red Hat Insights:** This is a product name and should generally be left as is. **Recommendation:** Unless you have a specific reason to do otherwise, I recommend using the first option: **Red Hat Insights MCP Server POC** This is the clearest and most concise translation for a technical audience.

MCP Serverless Functions Example

A basic example of developing and running serverless Model Context Protocol (MCP) using Netlify Functions, demonstrating how to deploy and access serverless functions with customized URLs.

LeetCode MCP (Model Context Protocol)

Okay, I understand. You want me to translate the phrase "MCP Server to generate Leetcode Notes" into Indonesian. Here's the translation: **Server MCP untuk menghasilkan Catatan Leetcode** Here's a breakdown of why this translation works: * **MCP Server:** This is kept as "Server MCP" because "MCP" is likely an acronym or proper noun and is often left untranslated. * **to generate:** This translates to "untuk menghasilkan" (to produce/to generate). * **Leetcode Notes:** This is translated to "Catatan Leetcode". "Notes" becomes "Catatan" (notes), and "Leetcode" is kept as is, as it's a proper noun. Therefore, the most natural and accurate translation is: **Server MCP untuk menghasilkan Catatan Leetcode**

CHM to Markdown Converter

Konversi CHM ke Markdown.

Exchange Rate MCP Server

Server MCP mainan yang menyediakan akses ke data nilai tukar dari API Norges Bank.

MCP Simple Server

Server sederhana yang mengimplementasikan Protokol Konteks Model untuk pencarian dokumen.

MCP Gateway

Gerbang berbasis plugin yang mengatur MCP (Modul Kontrol Proses) lainnya dan memungkinkan pengembang untuk membangun agen kelas perusahaan di atasnya.

MCP-Hub-MCP Server

A hub server that connects to and manages other MCP servers, allowing users to bypass Cursor's 40-tool limit and reduce AI mistakes by hiding infrequently used tools.

MCP Server Implementations

Implementasi server khusus untuk Model Control Protocol (MCP) menggunakan Server-Sent Events (SSE)

MCP YNAB Server 💰

Custom MCP server for YNAB API (TypeScript)

MCP Host Installation

Oke, saya mengerti. Berikut adalah terjemahan dari teks tersebut ke dalam bahasa Indonesia: "MCP yang menginstal MCP lain. MCP terakhir yang akan Anda instal secara manual dengan menambahkan perintah ke mcp.json Anda. Tambahkan MCP ini ke host favorit Anda dan minta untuk menginstal server apa pun yang Anda inginkan."

Model Context Protocol (MCP) MSPaint App Automation

Okay, here's a conceptual outline and a simplified example of how you might approach creating a Model Context Protocol (MCP) server and client to solve math problems and display the solution in MSPaint. This is a complex task, and this example focuses on the core communication and process execution. It's not a fully functional, production-ready system, but it provides a starting point. **Important Considerations:** * **MCP (Model Context Protocol):** MCP is not a standard, widely-used protocol. I'm assuming you're using it as a general term for a custom communication protocol. You'll need to define the exact message format and structure for your MCP. * **Security:** This example doesn't include any security measures. In a real-world application, you'd need to implement authentication, authorization, and encryption. * **Error Handling:** The error handling is basic. You'll need to add more robust error handling for production use. * **MSPaint Automation:** Automating MSPaint directly can be tricky and unreliable. A better approach might be to generate an image file (e.g., PNG) programmatically and then simply open it with the default image viewer (which might be MSPaint). * **Math Solving:** This example uses a very basic math evaluation. For more complex problems, you'll need a dedicated math library (e.g., SymPy in Python). **Conceptual Outline:** 1. **MCP Definition:** * Define the message format for requests (client to server) and responses (server to client). For example: * Request: `MATH: <math_expression>` * Response: `SOLUTION: <solution_string>` or `ERROR: <error_message>` 2. **Server:** * Listens for incoming connections on a specific port. * Receives math expressions from clients. * Evaluates the expression (using a math library or simple evaluation). * Generates a solution string. * Creates an image of the solution (using a library or by writing to a file that MSPaint can open). * Sends the solution string back to the client. 3. **Client:** * Connects to the server. * Sends a math expression to the server. * Receives the solution string from the server. * Displays the solution (ideally by opening an image file). **Simplified Python Example (using sockets):** ```python # server.py import socket import subprocess # For running MSPaint (or opening an image) import os def evaluate_math(expression): """ Evaluates a simple math expression. Replace with a more robust math library for complex expressions. """ try: result = eval(expression) # WARNING: eval() can be dangerous! return str(result) except Exception as e: return f"Error: {str(e)}" def create_solution_image(solution, filename="solution.png"): """ Creates a simple image file with the solution. (Replace with a more sophisticated image generation library like Pillow) """ # This is a placeholder. In a real application, you'd use a library # to draw the solution text onto an image. with open("temp.txt", "w") as f: f.write(solution) # Create a dummy image file (replace with actual image generation) os.system(f"echo {solution} > {filename}") # This is a very basic example return filename def run_server(): host = '127.0.0.1' # Localhost port = 12345 server_socket = socket.socket(socket.AF_INET, socket.SOCK_STREAM) server_socket.bind((host, port)) server_socket.listen(1) print(f"Server listening on {host}:{port}") while True: conn, addr = server_socket.accept() print(f"Connection from {addr}") data = conn.recv(1024).decode() if not data: break if data.startswith("MATH:"): expression = data[5:].strip() solution = evaluate_math(expression) image_file = create_solution_image(solution) # Create the image response = f"SOLUTION: {solution}" conn.sendall(response.encode()) # Open the image with MSPaint (or the default image viewer) try: subprocess.Popen(['mspaint', image_file]) # Windows specific # For other OS, use the default image viewer: # os.system(f"open {image_file}") # macOS # os.system(f"xdg-open {image_file}") # Linux except FileNotFoundError: print("MSPaint not found. Make sure it's in your PATH.") except Exception as e: print(f"Error opening MSPaint: {e}") else: conn.sendall("ERROR: Invalid request".encode()) conn.close() if __name__ == "__main__": run_server() ``` ```python # client.py import socket def run_client(): host = '127.0.0.1' port = 12345 client_socket = socket.socket(socket.AF_INET, socket.SOCK_STREAM) try: client_socket.connect((host, port)) except ConnectionRefusedError: print("Server not running. Please start the server first.") return expression = input("Enter a math expression: ") request = f"MATH: {expression}" client_socket.sendall(request.encode()) data = client_socket.recv(1024).decode() print(f"Received: {data}") client_socket.close() if __name__ == "__main__": run_client() ``` **How to Run:** 1. **Save:** Save the code as `server.py` and `client.py`. 2. **Run the Server:** Open a terminal or command prompt and run `python server.py`. 3. **Run the Client:** Open another terminal or command prompt and run `python client.py`. 4. **Enter Expression:** The client will prompt you to enter a math expression (e.g., `2 + 2`). 5. **See the Result:** The server will evaluate the expression, create a (very basic) image file, send the solution back to the client, and attempt to open the image in MSPaint. The client will also print the received solution. **Explanation and Improvements:** * **Sockets:** The code uses Python's `socket` library for basic TCP communication. * **`eval()` (DANGER):** The `eval()` function is used to evaluate the math expression. **This is extremely dangerous in a real application because it can execute arbitrary code.** Never use `eval()` with untrusted input. Use a safe math parsing library like `ast.literal_eval()` for simple expressions or a full-fledged math library like SymPy for more complex ones. * **Image Generation:** The `create_solution_image` function is a placeholder. You'll need to replace it with code that actually draws the solution onto an image. Libraries like Pillow (PIL) are excellent for this. * **MSPaint Automation:** The `subprocess.Popen(['mspaint', image_file])` line attempts to open the image in MSPaint. This is Windows-specific. For cross-platform compatibility, you can use `os.system()` with the appropriate command for opening the default image viewer on each operating system (see the comments in the code). * **Error Handling:** The error handling is minimal. You should add `try...except` blocks to handle potential errors like network connection issues, invalid math expressions, and problems opening MSPaint. * **MCP Format:** The MCP format is very simple (just `MATH:` and `SOLUTION:` prefixes). You can make it more robust by including message IDs, checksums, and other metadata. * **Threading/Asynchronous:** For a more scalable server, use threading or asynchronous programming (e.g., `asyncio`) to handle multiple client connections concurrently. **Example using Pillow for Image Generation (replace `create_solution_image`):** ```python from PIL import Image, ImageDraw, ImageFont def create_solution_image(solution, filename="solution.png"): """Creates an image with the solution using Pillow.""" image_width = 400 image_height = 200 image = Image.new("RGB", (image_width, image_height), "white") draw = ImageDraw.Draw(image) # Choose a font (you might need to adjust the path) try: font = ImageFont.truetype("arial.ttf", 24) # Replace with your font path except IOError: font = ImageFont.load_default() text_color = "black" text_position = (20, image_height // 2 - 12) # Center vertically draw.text(text_position, solution, fill=text_color, font=font) image.save(filename) return filename ``` **To use the Pillow example:** 1. **Install Pillow:** `pip install Pillow` 2. **Replace** the `create_solution_image` function in `server.py` with the Pillow version. 3. **Make sure** you have a font file (like `arial.ttf`) in a location your script can access, or use `ImageFont.load_default()`. This revised example provides a more practical starting point for building your MCP server and client. Remember to address the security concerns and improve the error handling before using it in a real-world scenario. Also, carefully consider the complexity of the math problems you want to solve and choose an appropriate math library.

MCP Firebird

Sebuah server yang mengimplementasikan Protokol Konteks Model (MCP) dari Anthropic untuk basis data Firebird SQL, memungkinkan Claude dan LLM lainnya untuk mengakses, menganalisis, dan memanipulasi data dalam basis data Firebird secara aman melalui bahasa alami.

Voice Call MCP Server

Server Protokol Konteks Model yang memungkinkan asisten AI seperti Claude untuk memulai dan mengelola panggilan suara real-time menggunakan Twilio dan model suara OpenAI.

😎 Contributing

🔥🔒 Awesome MCP (Model Context Protocol) Security 🖥️