MCP, the Model Context Protocol, is an open standard that lets AI tools reach data and functions outside their training. Instead of building a separate integration for every combination of tool and data source, both sides speak the same language.
This matters for businesses because many employees already use AI tools. With MCP, those tools can answer questions about manuals, contracts or tickets without you uploading the documents into each tool. What matters most is who may retrieve what.
- MCP is an open standard: AI tools (clients) ask services (servers) for data and functions over one shared protocol instead of many one-off integrations.
- An MCP server for company data returns matching passages with their source to any tool that speaks MCP.
- MCP does not enforce security on its own: permissions, approvals and the path of data to the model provider remain your job.
What is MCP?
A language model only knows what is in its training and what you give it in the request. It does not know your internal knowledge. For an AI tool to answer questions about your data, it needs access to the sources.
That is what the Model Context Protocol is for. It defines how an AI tool asks a service, which data and functions the service offers and in which format the answer comes back. Think of MCP as a shared power socket: the AI tool is the appliance, the data source is the grid. With one standard you don't need a custom cable for every combination.
Since the handover to the Agentic AI Foundation, MCP is developed vendor-neutrally. The specification is public; the current version is dated 28 July 2026.
How the Model Context Protocol works
MCP distinguishes three roles. The host is the application you work in, such as a chat or developer tool. Inside it sits an MCP client that holds the connection. On the other side is an MCP server that provides data or functions. Messages use the JSON-RPC format, locally on one machine or over the network.
An MCP server can offer three kinds of building blocks:
| Building block | What it provides | Example |
|---|---|---|
| Resources | content and data as context | a manual, a wiki page, a record |
| Tools | functions the model can call | “search the knowledge base”, “create a ticket” |
| Prompts | prepared workflows for users | a “summarise this contract” template |
When they connect, client and server tell each other what they can do. The AI tool then sees a list of available tools and resources and decides for each question whether it needs one. Depending on the host, it asks for your permission first.
MCP or a classic API: what is the difference?
A REST API is also an interface to your data. The difference lies in who uses it and how much work the connection takes.
Classic API
- Every integration is programmed one by one.
- Made for your own applications and automations.
- The AI tool does not know the interface by itself.
Model Context Protocol
- One standard for many tools and sources.
- Made for AI tools that query data and functions.
- The tool learns on connect what the server offers.
MCP does not replace the API. For your own programs, webhooks and automations, the API remains the right route. MCP is the shortcut when an AI tool already understands the protocol: you just enter the server's endpoint instead of programming an integration.
What an MCP server does for company data
Without a standard, every AI tool needs its own connection to every data source. Three tools and four sources make twelve integrations to maintain. With MCP, each source provides one server, and every tool with an MCP client can use it.
For company knowledge, a server that searches rather than just passing files along is especially useful: it finds the passages that match a question across all connected sources and returns them with the document they come from. That way the AI tool can back its answers with sources instead of relying on its training alone. That is the job of a RAG engine.
Choose sources which repositories the tool should knowProvide the server build it yourself or use a ready platformSet permissions who may retrieve what through the serverEnter the endpoint register the server in the AI tool
You can build your own MCP server with the open software libraries for common programming languages. For a single database that can make sense. If many sources need searching and answers need backing, a ready-made knowledge engine is usually in production faster.
Security and permissions with MCP
The MCP specification states plainly that the protocol cannot enforce security principles itself. Users should consent to every data access and action, and tools count as executable code to be treated with caution. For businesses this means the responsibility lies with whoever deploys servers and tools.
Three risks are worth knowing. A server with overly broad rights reveals more than the person asking should see. Ingested content can carry hidden instructions, a prompt injection. And the passages a server returns go to the AI tool and its model provider.
All points ticked. Well prepared.
With a local language model, the whole path stays in your environment. If the AI tool uses an external model, check contract and location there as you would with any other service provider.
MCP in RAGSuite
RAGSuite is both: an MCP server through which your AI tools query the knowledge engine and get matching passages with their source, and an MCP client through which you connect external MCP sources as knowledge. Both are included in the Community Edition at no extra cost.
The MCP server runs in your RAGSuite instance, in RAGSuite Engine in a data centre in Germany or on your own server. Access follows the permissions of your RAGSuite account, and the AI tool only gets the passages that match a question, not your whole collection. To connect, you enter your instance's MCP endpoint in the AI tool.
The MCP server page shows the details. The connectors page lists the sources you can connect, and pricing shows what RAGSuite Engine costs.
See for yourself how RAGSuite answers with sources as an MCP server: 7 days free in RAGSuite Engine or in a call with Sanjay.
Frequently asked questions
What is MCP, in simple terms?
MCP, the Model Context Protocol, is an open standard that lets AI tools query data and functions. A tool with an MCP client connects to an MCP server and learns which data, tools and prompts it offers. That way not every combination of tool and data source needs its own integration.
Who developed MCP?
Anthropic introduced the Model Context Protocol as an open standard in November 2024. In December 2025, MCP was handed to the Agentic AI Foundation, which sits under the Linux Foundation. Since then the standard has been developed vendor-neutrally; the specification is public.
What is an MCP server?
An MCP server is a service that provides data and functions over the Model Context Protocol. AI tools query it instead of needing a separate connection to each data source. An MCP server for company data returns, for example, matching passages from your documents together with their source.
Is MCP secure?
MCP handles access but does not enforce security rules. Safe use comes from limited permissions, approved sources, confirmation before actions, logs and a vetted model provider. Also watch for hidden instructions in ingested content, so-called prompt injections.
Does MCP replace a REST API?
No. A REST API remains the right route for your own programs, webhooks and automations. MCP is the easy route when an AI tool already understands the protocol: you just enter the server endpoint. Many teams use both, depending on the task.
Sources
- Model Context Protocol: specification (version 28 July 2026): roles, building blocks, security principles
- Anthropic: Introducing the Model Context Protocol (25 November 2024): introduction as an open standard
- Anthropic: Donating the Model Context Protocol (9 December 2025): handover to the Agentic AI Foundation
- OWASP Top 10 for LLM Applications 2025: LLM01 prompt injection
Sanjay is CTO at RAGSuite. He writes about architecture, self-hosting, open-source models and AI security.