Hosted MCP Servers: RAG for Your AI Agents Without the Infrastructure Headaches

Hosted MCP Servers: RAG for Your AI Agents Without the Infrastructure Headaches

Today we’re excited to launch Hosted MCP Servers, giving developers a way to add accurate RAG capabilities to AI agents using MCP in just a few clicks. Literally seconds.

Refer to our MCP docs for more.

What are MCP Servers?

The Model Context Protocol (MCP) from Anthropic is quickly gaining traction as a standard for connecting AI systems together. It’s being adoped by Google, OpenAI, and many, many others.

People are refering to it as the USB-C Adaptor for AI, providing a standard for connecting one AI system to another. Looking at this graph from star-history.com (which tracks GitHub repository popularity), interest in the Model Context Protocol repository has shown exponential growth over the past couple months:

So why is CustomGPT.ai introducing Hosted MCP Servers?

With MCP adoption exploding, it’s clear the missing piece is: a production-grade implementation that connects the Model Context Protocol (MCP) with Retrieval-Augmented Generation (RAG) for business data access without requiring DevOps expertise.

We’ve built exactly that, creating a bridge between these two powerful technologies with our industry-leading RAG system.

Let’s look at what makes this combination so powerful. By bringing them together, we’re creating something that’s more valuable than either alone:

1. Model Context Protocol (MCP) – The standardized API specification that allows AI agents to request external data during execution
2. Retrieval-Augmented Generation (RAG) – The technique that enhances Generative AI by retrieving only the most accurate and relevant information from your documents

CustomGPT.ai’s Hosted MCP Servers deploy production-ready RAG system as a fully managed service with an MCP-compliant endpoint. This means AI agents can access your business data without you needing to build, deploy, or maintain the underlying infrastructure.

Our service handles:

• RAG-Powered MCP Endpoint – A fully managed endpoint that lets your AI agents query your business data using the MCP standard, with our production-grade RAG technology running behind the scenes
• Server Infrastructure – Automatic provisioning, scaling, and high-availability configuration
• Security & Compliance – SOC-2 Type 2 certification with encryption in transit and at rest
• Document Processing & Indexing – Continuous monitoring and refreshing of your data sources when content changes
• Performance Optimization – We handle all the fine-tuning of embedding models, reranking algorithms, and retrieval parameters so you get maximum accuracy without any configuration work

All you do is connect your data, enable the MCP server, and point your MCP-compatible tools at the endpoint.

That’s it.

Why Hosted MCP Servers Matter for Developers

If you’re building AI applications, you’ve likely encountered two persistent challenges:

1. Accuracy Problems

LLMs without access to your specific data can only guess at answers based on their training data. This leads to hallucinations when the model encounters questions about your business, products, or internal processes.

RAG solves this by retrieving your own documents at runtime and including them in the context. But implementing RAG correctly is complex – you need robust embeddings, efficient retrieval algorithms, and reranking to ensure accuracy.

Our RAG implementation has been independently benchmarked as the #1 most accurate solution, meaning your agents get the right information when they need it.

2. Infrastructure Complexity

To host a production-grade MCP server with RAG capabilities, you’d need to:

• Provision and manage infrastructure (e.g. Kubernetes clusters or cloud VMs) to run the server reliably
• Set up and maintain a vector database for storing and querying document embeddings
• Build a data ingestion and indexing pipeline to preprocess, chunk, embed, and update your data
• Handle TLS certificate management and renewals to ensure secure, encrypted access

These infrastructure tasks distract from what you really want to focus on – building useful AI applications.

With Hosted MCP Servers, this entire infrastructure layer is managed for you. You can skip straight to building value-adding features instead of yak-shaving servers.

A Standard for AI Connectors

The Model Context Protocol functions like a universal serial bus (USB) for AI systems. Just as USB-C standardized how devices connect to computers, MCP standardizes how AI agents connect to data sources and tools. Instead of building custom connectors, developers can use a single, consistent protocol.

This standardization delivers three immediate benefits:

1. Plug-and-Play Data Access – AI agents can query exactly the information they need at runtime, similar to how a USB device automatically works when plugged in
2. Ecosystem Compatibility – Any MCP-compatible tool can instantly communicate with any other MCP-compatible system
3. Real-Time Information – No more stale data snapshots; agents connect to live sources for the most current information

Hosted MCP Servers bring these benefits to any MCP-compatible system, which now includes dozens of tools:

• AI Assistants like Claude Desktop, ChatGPT (with MCP plugin), and other MCP-enabled LLMs
• Agentic Frameworks like CrewAI, LangGraph, and AutoGen
• Workflow Automation Tools including n8n, Zapier, and Apache Airflow
• LLM Orchestration Frameworks such as LangChain, Haystack, and LlamaIndex
• Chatbot Builders like Voiceflow

How to Get Started with Hosted MCP Servers

Setting up your own Hosted MCP Server takes just a few minutes:

1. Start a Free Trial of CustomGPT.ai (or login if you already have one)
2. Create a new agent or select an existing one
3. Connect your data sources (PDFs, Google Drive, Notion, Confluence, and hundreds more)
4. Deploy the MCP Server by clicking Deploy → MCP → Enable (just 1 click!)
5. Copy the endpoint and JSON schema that’s generated for you
6. Configure your MCP-aware tool with the endpoint information

Check out my video on YouTube for a quick walkthrough!

That’s it – your AI agents can now access your business data through a fully managed MCP endpoint.

Conclusion

If you can’t tell, we’re super excited about this release. Hosted MCP Servers remove the main barriers to implementing RAG-powered AI agents:

• No infrastructure to manage
• Industry-leading retrieval accuracy
• Works with the growing ecosystem of MCP-compatible tools
• Deploy in minutes instead of weeks

If you’re building AI applications that need to access business data, you no longer need to choose between accuracy and development speed.

Try Hosted MCP Servers today – they’re included in all CustomGPT.ai plans, including the free trial.

Frequently Asked Questions (FAQ)

Is MCP Secure?
Yes. CustomGPT.ai’s Hosted MCP Servers are built with enterprise-grade security. The platform is SOC-2 Type 2 certified and your data is never shared or exposed; it’s accessed only via secure endpoints that you control. This ensures safe, real-time access to sensitive business information by your AI agents.

What’s the difference between MCP and RAG?
MCP (Model Context Protocol) is a universal standard—like “USB for AI”—that lets AI systems communicate with external tools and data in a consistent way. RAG (Retrieval-Augmented Generation), on the other hand, enhances AI accuracy by injecting real-time, relevant documents into model prompts. Hosted MCP Servers from CustomGPT.ai merge the two: you get a fully managed RAG pipeline behind an MCP-compliant endpoint, allowing your AI agents to fetch accurate business data without building custom infrastructure.

Do I need to know how to code to use Hosted MCP Servers?
Not at all. The CustomGPT.ai platform is entirely no-code. With just a few clicks, you can connect your data sources, enable an MCP Server, and start integrating with your AI workflows. Developers can plug the resulting endpoint into any MCP-aware system, but no programming is needed to get started.

What types of data sources can I connect?
CustomGPT.ai supports hundreds of data integrations including PDFs, Google Drive, Notion, Confluence, SharePoint, Dropbox, and many others. The platform continually updates indexed content and supports custom ingestion pipelines for enterprise environments. If your system isn’t listed, our team can help create a custom connector.

How quickly will my data be available to AI agents?
Indexing happens in near real-time. Once you connect your data, it’s typically available to AI agents within minutes via the MCP endpoint. The system also monitors for updates and automatically refreshes the index to ensure your agents are accessing the most current content.

Can I use Hosted MCP Servers with models other than Claude?
Yes. CustomGPT.ai is MCP-compliant and model-agnostic. You can use it with any AI model or framework that supports the Model Context Protocol, including OpenAI (via plugin), Claude, LangChain, LangGraph, AutoGen, and others. Hosted MCP Servers integrate seamlessly with modern AI stacks.

What’s included in the free trial?
The free trial gives you full access to CustomGPT.ai’s Hosted MCP Servers and all platform features. You can index up to 1,000 documents and make unlimited calls to the MCP endpoint. This lets you fully test your RAG-enabled AI agents across real business scenarios before committing to a paid plan.

How does pricing work after the trial?
Hosted MCP Servers are included in all CustomGPT.ai subscription plans. Pricing scales based on document volume and usage, making it accessible for startups and cost-efficient for enterprise-scale applications. There are no hidden fees—everything from hosting to indexing to endpoint access is covered in your plan.

What makes CustomGPT.ai different from other RAG providers?
CustomGPT.ai consistently ranks as the most accurate RAG solution in independent benchmarks. Unlike piecemeal alternatives, it delivers a fully managed stack: high-performance embeddings, reranking, secure infrastructure, and native integration with MCP tools. Plus, it’s no-code and deploys in minutes, helping teams ship faster with less overhead.