Discover Awesome MCP Servers

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.

LDAP MCP Server by CData

LDAP MCP Server by CData

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.

extracto-mcp

Enables Claude and any MCP client to turn a URL plus a schema into validated, typed JSON without HTML parsing or hallucinated fields.

SMS-Activate MCP Server

MCP server for integrating with SMS-Activate service, enabling phone number operations, email activations, and account management for receiving SMS verification codes and temporary email addresses.

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.

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.

protein-mcp-server

A Model Context Protocol server providing programmatic access to 3D protein structural data from RCSB PDB, PDBe, and UniProt, enabling search, retrieval, comparison, and analysis of protein structures.

Holochain MCP Server

Enables AI agents to search and retrieve Holochain documentation, including developer guides, HDK functions, and HDI API references.

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.

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.

figma-mcp-lightweight

Enables executing any Figma Plugin API code via a single tool, providing full access to Figma node manipulation through natural language.

my-mcp-server2

A Model Context Protocol server built with FastMCP that features dynamic tool loading and modular management via a dedicated tool directory. It supports both stdio and HTTP transport modes, enabling efficient development and deployment of custom MCP tools.

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.

Proximo

Enables management of Proxmox VE infrastructure via its REST API and execution of commands inside LXC containers via SSH, with built-in trust layers for planning, audit, undo, and diagnosis.

MCP OpenVision

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

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.

sor-mcp-server

Exposes 3 meta-tools to efficiently search and execute 174 backend CRUD tools, reducing context token usage by ~98%.

QrVerloz MCP Server

QrVerloz lets AI agents create QR codes instantly — no account needed, active for 90 days, and claimable at any time to make them permanent. Retarget the destination URL whenever you need, without reprinting.

MCP Gatekeeper

Enables policy-enforced access to dangerous tools like file read/write/delete and shell execution, with approval workflows, risk classification, and audit logging.

Docsmith MCP

Enables processing of Excel, Word, and PDF documents through reading, writing, and metadata extraction. It leverages a secure Pyodide WebAssembly environment to handle document parsing with support for paginated results.

cognee MCP server

Enables Claude to interact with cognee for cognitive augmentation, supporting local or Smithery installation.

Namecheap Domains MCP

Integrates Namecheap's domain API with Cursor AI to check domain availability, get pricing, and register domains directly through the AI interface.

MCP Worktree Voting Server

Enables parallel implementation of tasks using git worktrees, allowing you to create multiple variants of a solution, evaluate them side-by-side, and select the best one.

Revit MCP Server

Allows AI assistants to interact with Autodesk Revit through the MCP protocol, enabling the AI to create, modify, and delete elements in Revit projects.

Digital Guitar Archive MCP Server

Enables AI assistants to search and retrieve records from the Digital Guitar Archive database using full-text search and field-specific filters. It provides tools for fetching detailed record information and listing historical source libraries.

Hangfire MCP Server

Enables monitoring and managing Hangfire background jobs, including job lists, retries, recurring tasks, and statistics, directly from VS Code Copilot and other MCP clients.

sitemd

Static site generator / website building toolkit for AI coding agents like Claude, Codex, Cursor, Gemini, OpenClaw, etc. No subscription, no lock-in — host your site anywhere.

Bing Ads MCP Server

Production-grade MCP server for Microsoft Advertising (Bing Ads) API. Enables Claude to manage Bing/Microsoft Ads accounts with full campaign, ad group, keyword, and performance analysis capabilities.