Discover Awesome MCP Servers

😎 Contributing

🔥🔒 Awesome MCP (Model Context Protocol) Security 🖥️

PentestThinkingMCP

An AI-powered penetration testing reasoning engine that provides automated attack path planning, step-by-step guidance for CTFs/HTB challenges, and tool recommendations using Beam Search and MCTS algorithms.

Cold Email Assistant

Automates cold email outreach for job applications by parsing job postings, generating personalized emails using AI, and sending them or saving as drafts in Gmail with resume attachments.

Vercel MCP Server Template

A starter template for deploying Model Context Protocol (MCP) servers on Vercel using TypeScript and Vercel Functions. It includes example tools for rolling dice and checking weather to demonstrate tool integration patterns.

Directmedia MCP

Provides programmatic access to the Directmedia Publishing 'Digitale Bibliothek' collection, a 1990s German electronic book library containing 101 volumes of classic literature and philosophy with text extraction, search, and navigation capabilities.

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.

MCP Adobe Experience Platform Server

A Node.js server that provides a comprehensive API interface for Adobe Experience Platform (AEP) integration. It enables users to manage schemas, datasets, segments, and profiles while supporting data ingestion and query services.

Safari MCP Server

Native Safari browser automation for AI agents. 80 tools via AppleScript — zero overhead, keeps logins, runs silently in background. Drop-in alternative to Chrome DevTools MCP with 40-60% less CPU/heat on Apple Silicon.

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.

Agent MCP BrightData

An intelligent agent using the Model Context Protocol to iteratively explore and analyze websites in a structured way, with built-in duplicate protection and conversational interface.

Obsidian Translation MCP Server

Enables Claude to translate, search, and manage Obsidian notes directly through the Model Context Protocol. It features automatic backups, CRUD operations, and protects metadata and code blocks during the translation process.

MCP System Info Server

A lightweight MCP server that provides real-time hardware statistics including CPU, memory, disk, and NVIDIA GPU usage. It enables users to monitor system performance and retrieve comprehensive host machine specifications through a standardized interface.

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**

Bilibili MCP Server

Enables interaction with Bilibili (B站) platform through API and web scraping. Supports video search, article search, video info retrieval, comment fetching, danmaku extraction, and article content access.

Text-Toolkit

An MCP server that provides text conversion, formatting, and analysis functions, which can be directly integrated into the development workflow.

Google Docs MCP Server

Enables AI assistants to create, read, edit, and manage Google Docs and Drive files with support for formatting, comments, tables, images, and bulk operations.

MCP Perplexity Server

Provides AI-powered search, research, and reasoning capabilities through integration with Perplexity.ai, offering three specialized tools: general conversational AI, deep research with citations, and advanced reasoning.

Square MCP Server by CData

This read-only MCP Server allows you to connect to Square data from Claude Desktop through CData JDBC Drivers. Free (beta) read/write servers available at https://www.cdata.com/solutions/mcp

kintone MCP Server (Python3)

Enables AI assistants to interact with kintone data by providing comprehensive tools for record CRUD operations, file management, and workflow status updates. It supports secure authentication and automatic pagination to handle large datasets efficiently through the Model Context Protocol.

Gelbooru MCP

A Python MCP server that wraps the Gelbooru API. Connect it to any MCP-compatible client (Claude Desktop, Cursor, etc.) to search posts, look up tags, and generate Stable Diffusion prompts from real character appearance data — all directly from your AI assistant.

MCP Adapter

Automatically converts OpenAPI specifications into Model Context Protocol applications, enabling HTTP APIs to be managed as MCP services. It features a dynamic architecture that monitors file systems or Kubernetes ConfigMaps to update MCP tools in real-time.

POHODA MCP Server

An MCP server that integrates with Stormware POHODA accounting software via the mServer XML API. It provides 48 tools to manage invoices, stock, orders, warehouse documents, and accounting reports through any MCP-compatible client.

TWSE MCP Server

台灣證交所MCPServer

ethereum-validator-queue-mcp

An MCP server that tracks Ethereum’s validator activation and exit queues in real time, enabling AI agents to monitor staking dynamics and network participation trends.

MySQL MCP Server

A lightweight MySQL MCP server that enables LLMs to interact with databases through tools for schema inspection and query execution. It features LLM-friendly formatting, SSL support, and a secure read-only mode with query timeout protections.

EduChain MCP Server

Integrates the EduChain library with Claude Desktop to generate educational content such as multiple-choice questions, lesson plans, and flashcards. It utilizes Gemini LLMs through LangChain to provide local and secure content generation tools.

SAP Ariba Procurement MCP Server by CData

This project builds a read-only MCP server. For full read, write, update, delete, and action capabilities and a simplified setup, check out our free CData MCP Server for SAP Ariba Procurement (beta): https://www.cdata.com/download/download.aspx?sku=PAZK-V&type=beta

RAG MCP Server

Combines a knowledge graph with RAG (Retrieval-Augmented Generation) capabilities for semantic code indexing and search. Enables creating entity relationships, managing observations, and performing semantic searches across indexed codebases.

MCP Resume Chat Server

Enables AI-powered conversations about resume/CV content and email notification sending through a comprehensive MCP server. Features a modern Next.js frontend with resume chat interface, email forms, and resume viewer for SE interview demonstrations.