Discover Awesome MCP Servers

lmstudio-mcp

Enables Claude Code to delegate mechanical tasks (summaries, boilerplate, reformatting) to local models running in LM Studio.

Minimalist Knowledge Base MCP

Enables LLMs to manage file-based knowledge bases with dual storage (Markdown + SQLite). Supports creating, searching, and organizing articles across multiple knowledge bases with full-text search capabilities.

code-rag-mcp

Enables semantic search over codebases using natural language queries, returning relevant code snippets with source locations. Integrates with Claude Code for automatic codebase exploration.

CTRL MCP

The on-chain automation layer. Live on Base. Automate your workflows with the CTRL MCP. Sign once. Execute forever.

Review Analysis MCP Server

A local, privacy-friendly pipeline for analyzing customer reviews using Ollama for sentiment analysis and issue clustering.

MapBiomas Soil MCP

Enables querying soil data, planning and executing Earth Engine processing recipes, monitoring tasks, and answering questions about results without downloading national maps.

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.

Publora MCP Server

Official MCP server for Publora that enables AI assistants to schedule posts, manage accounts, and retrieve analytics across multiple social media platforms through natural language.

mcp-gateway

A generic MCP gateway that aggregates multiple upstream MCP servers into a single FastMCP endpoint, configured via servers.json with support for tool subsetting, renaming, multi-instance routing, and pluggable authentication.

La Growth Machine — B2B Outreach & Pipeline Analytics

Analyze LinkedIn & email outreach campaigns, track pipeline performance, and review lead conversations for RevOps, Sales Managers, and SDR teams.

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.

baby-fever-simulator

MCP server for infant fever management that tracks symptoms, medication, and lab results, providing guideline-based escalation levels, pharmacokinetic predictions, and structured clinical reports.

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.

newrelic-mcp

Executes NRQL queries against New Relic via the NerdGraph API, enabling monitoring and observability data retrieval through natural language.

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.

meetergo MCP server

Enables AI agents to manage Meetergo calendars by finding slots, booking, rescheduling, and canceling appointments.

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 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.

deepseek-vision-mcp

Provides a recognize_image tool that enables image recognition using DeepSeek's vision capabilities. Supports automatic login via browser or manual token configuration for server environments.

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.

mailpal-mcp-server

Free email server for AI agents with hardware attestation, enabling agents to send, receive, and manage emails via MCP tools.

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.

MCP Toolkit

A collection of production-ready MCP servers for PostgreSQL, SQLite, Redis, File System, and GitHub API, enabling database operations, file management, and GitHub interactions through natural language.

Graph Uniswap MCP

Provides a unified MCP interface over Uniswap V2, V3, and V4 across multiple chains via The Graph, enabling agents to query token prices, top pools, pairs, and recent swaps with clean JSON output.

SearXNG Control Plane MCP Server

An MCP server for managing SearXNG instances, enabling configuration inspection, engine toggling, health checks, and container lifecycle control.

LinkedIn Profile Data Mining

Enables comprehensive LinkedIn profile search, data extraction, and contact enrichment using Google Search, Apollo.io, and AI-powered analysis. Includes automated data mining workflows with database storage and CSV export capabilities.

MCP Stateless Server Template

A production-ready stateless MCP server template implementing the 2026-07-28 specification, enabling horizontal scaling and easy migration from session-based servers.

apexapi-mcp

Enables calling 120+ AI models and reading live web pages (scrape, crawl, structured extract) from any MCP client using one API key.