Awesome MCP Servers: Top 15 for 2025

15 awesome MCP servers for LLM apps: Integrate enterprise data, enable RAG, and safely trigger AI tools. Compare features and find well-documented options. 

MCP in brief 

With the rise of LLM-powered apps, it’s become clear that feeding LLMs with structured, contextual information at runtime is critical for accuracy and personalization – and the Model Context Protocol (MCP) has quickly emerged as the standard to make that possible. 

What makes an MCP server awesome

Within the MCP, an MCP server acts as the hub between generative AI (GenAI) apps (MCP clients) and enterprise data sources. Its primary function is to receive data requests from clients, securely retrieve the relevant data and information from various backend systems (databases, APIs, documents, files, etc.), enforce data privacy and security policies (like masking or filtering), and then deliver the processed data back to the requesting client in a structured manner and conversational latency.

The MCP server orchestrates the complex data retrieval process, leveraging the metadata of the underlying sources along with an LLM to understand which and how sources should be queried. The MCP server is typically required to combine data from multiple sources, and ensures that only authorized data is returned to the AI application.  

This crucial function enables a GenAI app to ground its responses in live, enterprise-specific data, enhancing accuracy and personalization, while maintaining data governance. 

Awesome MCP servers analyzed 

I've spent the past few months exploring and testing dozens of MCP servers – open-source and commercial; production-grade and experimental.  

In this guide, I’ve pulled together a list of the top 15 Awesome MCP Servers across a range of use cases, from enterprise data and knowledge, to dev tools, public APIs, and more.  

Whether you're looking to enable Retrieval-Augmented Generation (RAG) to integrate internal docs, fetch CRM and billing data for your RAG chatbot, or feed structured multi-source enterprise data to an LLM through Table-Augmented Generation (TAG), this list includes a variety of MCP servers that are robust, well-documented, and already being used in the field.

Below, you’ll find a comparison table covering features, hosting options, open-source status, and further on more details about each of these awesome MCP servers.  

The 15 most awesome MCP servers for 2025 

Top 15 awesome MCP servers in detail 

1. K2view MCP server 

K2view provides a high-performance MCP server designed for real-time delivery of multi-source enterprise data to LLMs. Using entity-based data virtualization tools, it enables granular, secure, and low-latency access to operational data across silos. 

Main features: 

• Real-time data delivery from multiple systems 

• Granular data privacy and security 

• Built-in data virtualization and transformation 

• On-prem and cloud-ready deployments 

Resources: 

• Installation intro

• Setup guide
  
  

2. Vectara MCP server 

Vectara offers a commercial MCP server designed for semantic search and retrieval-augmented generation (RAG). It enables real-time, relevance-ranked context delivery to LLMs using custom and domain-specific embeddings. 

Main features: 

• RAG framework with semantic search

• Automated generation of embeddings

• Supports multi-language queries

• API-first and open-source reference MCP server 

Resources: 

• Vectara MCP server (Github)  

• MCP server overview
  
  

3. Zapier MCP server 

Zapier’s MCP server enables LLMs to interact with thousands of apps, ranging from Google Sheets to simple CRMs. It exposes Zapier workflows, triggers, and automations to GenAI systems. 

Main features: 

• Access to 6,000+ integrated apps

• Trigger actions by MCP clients  

• No-code automation builder 

• Hosted cloud-based context delivery 

Resources: 

• Zapier MCP server overview  

• Blog intro
  
  

 4. Notion MCP server

This MCP server exposes Notion data (pages, databases, tasks) as context to LLMs, allowing AI agents to reference workspace data in real-time. It’s a practical tool for knowledge assistants operating within productivity tools. 

Main features: 

• Access pages, databases, and tasks via MCP 

• Contextual snapshot of teams’ workspace 

• Self-hosted server with OAuth integration 

• Ideal for multi-user knowledge management 

Resources: 

• Notion MCP server 

• GitHub repository
  
  

5. Supabase MCP server 

The Supabase MCP Server bridges edge functions and Postgres to stream contextual data to LLMs. It’s built for developers who want server-less, scalable context delivery, based on user or event data. 

Main features: 

• Postgres-native MCP support 

• Edge Function triggers for live updates 

• Integration with RLS and auth 

• Open-source and self-host 

Resources: 

• Supabase blog intro 

• GitHub repository

• Docs
  
  

6. Pinecone MCP server 

Built on Pinecone’s vector database, this MCP server supports fast, similarity-based context retrieval. It’s optimized for applications that require LLMs to recall semantically relevant facts or documents.

Main features: 

• Fast vector search, optimized for similarity 

• Scalable retrieval   

• Embedding-based document indexing 

• Production-grade latency and reliability 

Resources: 

• GitHub repository
  
  

7. OpenAPI MCP server by Hugging Face 

A community-built OpenAPI MCP server designed to enable transparent, standardized access to LLM context. It demonstrates interoperability between LLM tools and open data resources. 

Main features: 

• Standardized interface for OpenAPI-based APIs 

• Lightweight demo implementation 

• Supports HuggingFace Spaces deployment  

• Ideal for community experimentation 

Resources: 

• Install guide / blog
  
  

8. Slack MCP server 

The Slack MCP Server captures real-time conversation threads, metadata, and workflows, making them accessible to LLMs. It’s used in enterprise bots and assistants for enhanced in-channel responses. 

Main features: 

• Thread and channel context injection 

• Contextual memory for assistant responses 

• Integrated with Slackbot and slash commands 

• Enterprise-ready, no self-hosting required 

Resources: 

• Slack MCP server guide
  
  

9. Salesforce MCP connector 

Salesforce’s MCP integration enables CRM data (accounts, leads, conversations) to be injected into LLM workflows. It supports AI use cases in marketing, sales enablement, and service automation. 

Main features: 

• CRM entity access (leads, opportunities, tasks) 

• Role-based context customization 

• Integration with Service Cloud AI 

• Secure, enterprise-grade deployment

Resources: 

• Marketing, cloud, connect, and install docs

• Setup guide
  
  

10. LangChain MCP server 

LangChain includes support for building full-featured MCP servers that allow AI agents to dynamically query knowledge bases and structured data. It includes out-of-the-box integrations and adapters. 

Main features: 

• Agent-ready framework with MCP adapters 

• Plug in external tools with ease 

• Extensible for autonomous workflows 

• Powered by composable chains and tools 

Resources: 

• MCP agent setup guide 

• Beginner tutorial
  
  

11. LlamaIndex MCP server 

LlamaIndex enables users to create MCP-compatible context servers that pull from structured and unstructured data sources (e.g., docs, APIs). It supports fine-grained context retrieval pipelines. 

Main features: 

• Unified context retrieval framework 

• Modular loaders for files, APIs, and DBs 

• Graph, vector, and keyword-based retrievers

• Fine-tuned for RAG and agent orchestration 

Resources: 

• Install docs 

• LlamaHub plugins
  
  

12. Databricks MCP server (via Mosaic) 

Databricks supports MCP integration through its Mosaic framework, connecting Delta Lake and ML pipelines to LLMs. It’s focused on enabling high-scale, enterprise-grade data context for AI.

Main features: 

• Direct integration with Delta Lake 

• AI-ready pipelines with Spark and MLflow 

• High-scale data preparation for context 

• Supports enterprise use cases with notebooks 

Resources: 

• Mosaic install docs 

• Model serving
  
  

13. MCP Weather server (reference implementation)

This official reference MCP server simulates weather data being delivered as context to LLMs. It’s great for understanding how to implement the MCP spec with structured APIs. 

Main features: 

• Reference implementation for time-series APIs 

• Simple plug-and-play design for public APIs 

• Compatible with MCP client SDKs 

• Good learning example for less experienced developers

Resources: 

• Quickstart server tutorial
  
  

14. OKX MCP server (finance demo) 

The OKX MCP Server is a demo project that shows how to deliver cryptocurrency and market data via MCP. It’s useful for LLMs offering real-time financial advice or analytics. 

Main features: 

• Real-time crypto market data 

• Access to tickers, trades, and order books 

• High-frequency update support 

• Open-source, fast to deploy locally 

Resources: 

• GitHub repository
  
  

15. Google Calendar MCP server 

This experimental server exposes Google Calendar data to LLMs through MCP. It allows assistants to reason over schedules, availability, and meeting metadata in natural language. 

Main features: 

• Access to events, availability, schedules 

• Context delivery based on time ranges 

• Secure OAuth authentication 

• Great for productivity and scheduling agents 

Resources: 

• Google Calendar MCP example 

Awesome MCP server wrap-up

To summarize , awesome MCP servers securely connect GenAI apps with enterprise data sources. They enforce data policies and deliver structured data with conversational latency, enhancing LLM response accuracy and personalization while maintaining governance. The most awesome MCP servers provide flexibility, extensibility, and real-time, multi-source data integrations.

Read the practical guide to Model Context Protocol
(MCP) – free of charge – before you choose.