Discover Awesome MCP Servers

MCP Server Template

A starter template for creating Model Context Protocol servers that integrate with Claude Desktop and other MCP-compatible clients. Provides a configurable tool system, Docker support, and TypeScript-ready structure for building custom MCP servers.

Human-Controlled MCP Server

Connects Claude to a human operator through a web interface, allowing Claude to ask questions, request searches, or seek decisions that are answered by a human in real-time. Instead of automated tools, Claude gets responses directly from you through a beautiful web dashboard.

Zhipu Web Search MCP

Berdasarkan alat pencarian web dari Zhipu AI.

linkedmcp

linkedmcp

cascade-mcp-server

Repositori Server Cascade MCP

Data Commons MCP Server

Enables AI agents to query and retrieve public statistical data from Data Commons through search and observation tools. Provides access to demographic, economic, and other statistical indicators for analysis and research.

ModelScope Image MCP Server

Enables users to generate high-quality images using ModelScope's Qwen-Image model through natural language prompts. Supports async task processing with both image URL and base64 encoded data output options.

WeatherXM Pro MCP Server

Provides comprehensive access to real-time weather data, forecasts, historical data, and weather alerts from the WeatherXM decentralized weather network worldwide.

MCP Google Sheets Server

Enables reading, writing, and managing Google Sheets documents with support for batch operations, formatting, charts, and conditional formatting through the Model Context Protocol.

Document Reader MCP Server

Enables reading and processing various document formats including Word, PDF, RTF, and text files. Supports extracting media elements like images and links, with features for PDF page range selection and automatic text encoding detection.

MCP Dust Server

Server Protokol Konteks Model yang memungkinkan interaksi dengan agen Dust AI, memungkinkan integrasi dengan lingkungan pengembangan seperti Windsurf IDE dan Claude Desktop.

Messages MCP Server

A Model Context Protocol server that provides access to messages from an external API endpoint using Bearer token authentication for integration with Claude Desktop.

H3 CLI MCP Server

An MCP server that allows AI assistants and LLMs to interact with the Horizon3.ai API for scheduling pentests, querying results, and automating security workflows through natural language commands.

MCP SageMath Server

Enables execution of SageMath mathematical computations through a local SageMath installation. Provides tools to check SageMath version and evaluate SageMath scripts with configurable timeouts and error handling.

LDAP MCP Server by CData

LDAP MCP Server by CData

mcp-korean-spell

There isn't a widely known or readily available "MCP server" specifically designed for Korean spell checking. The term "MCP server" doesn't directly relate to Korean spell checking technology. However, here's a breakdown of how you can achieve Korean spell checking functionality and some related concepts: **Understanding the Components** * **Spell Checking Engine:** This is the core software that analyzes text and identifies potential spelling errors. * **Dictionary:** A comprehensive database of correctly spelled Korean words. * **Grammar Rules:** Rules that define correct Korean grammar and sentence structure. * **API/Server (Optional):** A way to access the spell checking engine programmatically. This allows you to integrate spell checking into your applications or websites. **How to Achieve Korean Spell Checking** 1. **Online Korean Spell Checkers:** * **부산대학교 인공지능연구실/나라인포테크:** This is a very popular and reliable online Korean spell checker. You can find it by searching for "부산대 맞춤법 검사기" or "Korean spell checker PNU" (PNU stands for Pusan National University). It's often considered the standard. * **한글 맞춤법/문법 검사기 (Korean Spell/Grammar Checker):** Another online option. Search for "한글 맞춤법 검사기" to find various online tools. 2. **Software with Built-in Korean Spell Checkers:** * **MS Word:** Microsoft Word often has Korean language support, including spell checking. You may need to install the Korean language pack. * **Hangul (한글과컴퓨터):** This is a popular Korean word processor that includes robust Korean spell checking. 3. **APIs and Libraries (For Developers):** * **Kakao Developers:** Kakao (a major Korean tech company) *used* to offer a Korean spell checking API, but it's no longer publicly available. This is a common issue – these APIs can be discontinued. * **Naver Labs:** Naver (another major Korean tech company) might have internal tools, but they don't generally expose a public spell-checking API. * **Custom Solutions:** If you need a very specific solution, you might need to build your own spell checker using Korean language processing libraries (like KoNLPy in Python) and a large Korean dictionary. This is a complex undertaking. **Why "MCP Server" is Unlikely** The term "MCP server" doesn't have a direct connection to Korean spell checking. It's possible it's a very specific internal term used by a particular company or project, but it's not a standard term in the field. **In summary:** Focus on using the online spell checkers from Pusan National University or other reputable sources, or use software like Hangul or MS Word with Korean language support. If you need an API, you'll likely need to research and potentially build your own solution using Korean NLP libraries.

MCP OpenVision

A Model Context Protocol server that enables AI assistants to analyze images using OpenRouter vision models through a simple interface.

n8n-workflow-builder-mcp

n8n-workflow-builder-mcp

MCPserver

There isn't a single, universally recognized "MCP server" specifically designed for AI chat. The term "MCP" could refer to a few different things, so let's break down the possibilities and how they relate to AI chat in Indonesian: **Possible Meanings of "MCP" and their Relevance to AI Chat:** * **Most Critical Point (Project Management):** This is unlikely to be relevant to AI chat. * **Master Control Program (from the movie Tron):** This is a fictional concept and not a real server type. * **Minecraft Protocol (MCP):** This is a protocol used for Minecraft servers. It's *highly unlikely* to be directly used for AI chat. Minecraft servers are for running the Minecraft game, not for hosting AI chat applications. * **Misspelling/Typo:** It's possible "MCP" is a typo for something else. **What you're likely looking for is a server or platform to *host* an AI chat application.** Here's what you should consider: **Options for Hosting an AI Chat Application (that can handle Indonesian):** 1. **Cloud Platforms (Recommended):** These are the most common and scalable solutions. * **Google Cloud Platform (GCP):** Excellent for AI/ML. You can use their AI Platform to deploy models and their Compute Engine for general server needs. GCP supports Indonesian language models. * **Amazon Web Services (AWS):** Similar to GCP, AWS offers a wide range of services, including SageMaker for AI/ML and EC2 for virtual servers. AWS also supports Indonesian language models. * **Microsoft Azure:** Another major cloud provider with AI/ML services like Azure Machine Learning and virtual machines. Azure supports Indonesian language models. * **DigitalOcean:** A simpler and often more affordable cloud provider, good for smaller projects. You'd need to set up the AI chat application yourself on a virtual server. * **Vultr:** Similar to DigitalOcean, offering affordable virtual servers. 2. **Dedicated Server:** You can rent a physical server from a hosting provider. This gives you more control but requires more technical expertise. 3. **Virtual Private Server (VPS):** A middle ground between cloud platforms and dedicated servers. You get a virtualized server environment with more control than a cloud platform but less responsibility than a dedicated server. **Key Considerations for Choosing a Server/Platform for AI Chat (with Indonesian support):** * **Language Model Support:** The AI model you use *must* be trained on or capable of handling Indonesian. Look for models specifically designed for multilingual support or trained on Indonesian datasets. Examples include: * **Multilingual BERT (mBERT):** A popular multilingual model. * **IndoBERT:** A BERT model specifically pre-trained on Indonesian text. * **GPT-3/GPT-4 (via API):** These powerful models from OpenAI can handle Indonesian, but you'll need to use their API and pay for usage. * **Other Indonesian-specific models:** Research models specifically trained for Indonesian NLP tasks. * **Processing Power (CPU/GPU):** AI models, especially large language models, require significant processing power. Choose a server with enough CPU and potentially GPU resources. GPUs are especially important for training and inference with deep learning models. * **Memory (RAM):** The AI model and your application will need sufficient RAM to run efficiently. * **Storage:** You'll need storage for the AI model, your application code, and any data you need to store. * **Scalability:** If you expect a lot of users, choose a platform that can easily scale up resources as needed. Cloud platforms are generally best for scalability. * **Cost:** Compare the costs of different options, considering factors like CPU, RAM, storage, and bandwidth. * **Ease of Use:** Consider your technical skills. Cloud platforms offer more managed services, which can simplify deployment and management. * **API Integration:** If you're using a pre-trained model via an API (like OpenAI's GPT-3), ensure the server/platform you choose can easily integrate with the API. **Steps to Set Up an AI Chat Application (General Outline):** 1. **Choose an AI Model:** Select an AI model that supports Indonesian. 2. **Develop the Chat Application:** Write the code for your chat application. This will likely involve using a framework like Python (with libraries like Flask or Django) or Node.js. 3. **Choose a Server/Platform:** Select a cloud platform, VPS, or dedicated server. 4. **Deploy the Application:** Deploy your application to the chosen server/platform. This will involve setting up the server environment, installing dependencies, and configuring the application. 5. **Integrate the AI Model:** Connect your chat application to the AI model. This might involve using an API or loading the model directly into your application. 6. **Test and Optimize:** Thoroughly test your application and optimize its performance. **Example Scenario (Using Google Cloud Platform):** 1. **AI Model:** Use IndoBERT or a multilingual model like mBERT. 2. **Chat Application:** Develop a Python-based chat application using Flask. 3. **Platform:** Google Cloud Platform (GCP). 4. **Deployment:** * Create a Compute Engine instance (a virtual machine). * Install Python and necessary libraries (Flask, TensorFlow/PyTorch if needed). * Deploy your Flask application to the Compute Engine instance. * If using IndoBERT, download the model and load it into your application. * If using a cloud-based AI service (like Google's Dialogflow or Vertex AI), configure your application to communicate with the service. 5. **Testing:** Test the chat application to ensure it's working correctly and handling Indonesian input and output. **In summary, there's no specific "MCP server" for AI chat. You need to choose a server or platform that can host your AI chat application and support the AI model you're using, with a focus on Indonesian language capabilities.** Cloud platforms are generally the best option for scalability and ease of use. Remember to research and select an AI model that is trained on or capable of handling Indonesian.

WikiJS MCP Server

A Model Context Protocol server that enables Claude to read and update documentation in Wiki.js instances through capabilities like searching, reading, creating, and updating wiki pages.

LexLink

Enables AI systems to search, retrieve, and analyze Korean legal information from the National Law Information API (law.go.kr), including laws, administrative rules, English translations, and law-ordinance linkages.

MCP Inflow Ingredients

Enables AI assistants to interact with Inflow Inventory API for managing ingredients/products and inventory operations. Supports product creation, updates, search, and stock adjustments through natural language commands.

MCP Nano Banana

Enables image generation using the Google Gemini API through a simple text prompt. Generated images are saved locally and hosted online via ImgBB for easy access and sharing.

Xava Labs MCP Template

A template repository for building Model Context Protocol (MCP) servers that enables developers to create interactive AI agents with real-time bidirectional communication capabilities through WebSocket and SSE endpoints.

MCP Echo Service

Provides echo tools for testing MCP protocol functionality with message echoing, delayed responses, and JSON data analysis capabilities.

PlayFab MCP Server

Sebuah server yang memungkinkan LLM (seperti Claude dan VSCode Copilot) untuk berinteraksi dengan data Azure Cosmos DB melalui kueri bahasa alami, bertindak sebagai penerjemah antara asisten AI dan basis data Anda.

MCP Server for Cirro Data

This server enables multi-agent conversations for interacting with Cirro's biological data platform through its OpenAPI interface, auto-generated using AG2's MCP builder.

MCP Server TypeScript

A production-ready TypeScript MCP server providing basic tools (add, echo, timestamp), resources (server info, greetings, data access), and prompt templates (analyze, code-review, summarize). Serves as a foundation for building custom MCP servers with extensible architecture.