Discover Awesome MCP Servers

TeacherAssist-MCP

AI-powered Teacher Assistance MCP Server for managing student records with PostgreSQL, enabling create and read operations via MCP tools.

Agent Construct

An MCP server implementation that standardizes how AI applications access tools and context, providing a central hub that manages tool discovery, execution, and context management with a simplified configuration system.

SQL MCP Server

Enables natural language and SQL querying across SQLite, MySQL, PostgreSQL, and MongoDB databases with support for bulk operations and schema inspection. It features a safety mechanism requiring explicit user confirmation for destructive actions like data modification and deletion.

Swagger MCP

High-performance server for exploring Swagger/OpenAPI specifications with dynamic session management, lightning-fast endpoint search, and efficient caching. Enables AI assistants to discover, search, and generate code from REST APIs.

Greek Financial Regulation MCP

Query Greek financial regulation data — regulations, decisions, and requirements from HCMC and Bank of Greece — directly from MCP-compatible clients like Claude or Cursor.

@processon/mcp-server-processon-node

Generates ProcessOn mind maps from Markdown content. Provides tools to check API key validity and create mind maps.

FlexSim MCP Server

Controls FlexSim simulation engine via Model Context Protocol, enabling natural language-driven simulation modeling and automation.

mcp_repo_e2769bdc

Ini adalah repositori pengujian yang dibuat oleh skrip pengujian MCP Server untuk GitHub.

Remote MCP Server Authless

Enables deploying a remote MCP server on Cloudflare Workers without authentication, supporting custom tools and connectivity to Cloudflare AI Playground and Claude Desktop via mcp-remote proxy.

McpR

Here are a few ways to translate "Demo MCP Server using SignalR" into Indonesian, depending on the nuance you want to convey: **Option 1 (Most straightforward):** * **Demo Server MCP menggunakan SignalR** This is a direct translation and is perfectly understandable. It's suitable for most technical contexts. **Option 2 (Slightly more descriptive):** * **Demo Server MCP dengan menggunakan SignalR** This adds "dengan" (with) to emphasize the use of SignalR. **Option 3 (Focus on the demonstration aspect):** * **Demonstrasi Server MCP menggunakan SignalR** This uses "Demonstrasi" which is a more formal word for "demo" and might be appropriate for a presentation or formal documentation. **Option 4 (More natural sounding, but slightly less literal):** * **Contoh Server MCP menggunakan SignalR** This uses "Contoh" which translates to "Example" and can be used in place of "Demo" to make it sound more natural. **Which one should you use?** * If you want the most direct and common translation, use **"Demo Server MCP menggunakan SignalR"**. * If you want to emphasize the use of SignalR, use **"Demo Server MCP dengan menggunakan SignalR"**. * If it's a formal presentation, use **"Demonstrasi Server MCP menggunakan SignalR"**. * If you want a slightly more natural sound, use **"Contoh Server MCP menggunakan SignalR"**. In most cases, the first option is perfectly acceptable and widely understood.

App Store Connect MCP Server

A Model Context Protocol (MCP) server for Apple's App Store Connect API. Manage your iOS, macOS, tvOS, and visionOS apps directly from Claude, Cursor, or any MCP-compatible client.

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.

openEHR MCP Server

Enables MCP clients like Claude Desktop to interact with openEHR REST APIs (EHRbase) for creating compositions, managing EHRs, listing templates, and executing AQL queries.

Oli Docs MCP

Local MCP server for querying Oli/LimX documentation via keyword, vector, or hybrid search, with citation support for use in Claude Code or OpenCode/August.

Email MCP Server

An HTTP/SSE wrapper for the IMAP MCP server that enables users to read, search, and send emails across multiple accounts and providers. It supports secure AES-256 encryption and provides remote access through Claude Web using SSE transport.

IntelliSchedule Personal MCP Server

Enables scheduling and calendar management through Google Calendar and Cal.com, with support for reminders, notes, and email notifications.

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.

obsidian-mcp

Enables semantic search and note management for Obsidian vaults via the Model Context Protocol, allowing LLMs to search, read, and index notes, PDFs, and web pages locally.

Parsley MCP Server

Enables AI assistants to access buyer intent signals, MEDDIC qualification data, and lead intelligence from Parsley accounts for lead qualification and follow-up.

Devonthink MCP Server

This MCP server provides access to DEVONthink functionality via the Model Context Protocol (MCP). It enables listing, searching, creating, modifying, and managing records and databases in DEVONthink Pro on macOS.

mcp-aurekai

Aurekai MCP exposes 89 native akai_\* runtime operators to any MCP-compatible host, covering the full Aurekai binary family — API gateway, artifact inspection, proof bundle export, semantic embedding, batch queuing, entity detection, compression, and more. Zero external dependencies; runs locally via npx -y @aurekai/mcp.

MCP OpenVision

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

x402 MCP Server

An MCP server that provides DeFi data tools (crypto prices, whale concentration, funding rates) for AI agents via the x402 protocol.

Kaskad Protocol MCP Server

First-party MCP server for Kaskad Protocol — a DeFi lending protocol on Igra L2 (Kaspa). Enables AI agents to autonomously supply, borrow, repay, withdraw, and stake. Includes 16 tools covering live market reads, governance params, health factor monitoring, emission state, and full write access to on-chain lending operations. Compatible with Claude, OpenClaw, and any MCP-compatible client.

Polarion MCP Server

Integrates with Siemens Polarion ALM to manage test cases, test runs, and results, with support for JUnit import and spreadsheet export.

@striderlabs/mcp-grubhub

Enables AI agents to autonomously order food from Grubhub, including searching restaurants, browsing menus, managing cart, placing orders, and tracking delivery.

LDAP MCP Server by CData

LDAP MCP Server by CData

agent-docs-mcp

MCP server that provides AI coding agents automatic access to AGENTS.md documentation from GitHub repositories, enabling understanding of codebase conventions and patterns.

K-Data Gate MCP Server

Provides Korean market data (products, trends, stocks, real estate) in English JSON for AI agents, with 13 tools including search, trends, and stock analysis.