Model Context Protocol (MCP)
The Model Context Protocol (MCP) is an open standard originally introduced by Anthropic that sets how AI applications and language models are connected to external tools, data sources and systems. Rather than building a separate interface for every integration, MCP offers one uniform, reusable connection between AI hosts and so-called MCP servers. Capabilities and data can thus be connected to language models in a standardised way.
Also known as: MCP, Model Context Protocol
What is the Model Context Protocol (MCP)?
The Model Context Protocol, or MCP, is an open standard for connecting AI applications to external tools and data sources. Originally from Anthropic introduced, it has become a widespread method for giving language models controlled access to functions and information beyond their training knowledge.
The basic idea can be compared to a universal plug system: instead of coding a bespoke connection for every combination of AI application and external system, MCP defines a shared language. Integrating new data sources or tools thus becomes far simpler and reusable.
How is MCP structured?
MCP distinguishes two central roles. On one side is the MCP host, or MCP client, that is the AI application itself, for example a chat assistant or a AI agent. On the other side are the MCP serverthat provide concrete capabilities.
An MCP server can offer three kinds of building block: tools, that is, executable functions such as sending an email or querying a database; resources, that is, data and documents the model can read; and prompts, that is, predefined templates for recurring tasks. The host discovers these building blocks and uses them through the standardised protocol.
What are the benefits of MCP?
The greatest advantage is standardisation. Before MCP, every connection between an AI application and an external system had to be built individually, which with many systems quickly produced a confusing tangle of one-off integrations. MCP replaces that multitude with one uniform, reusable Interface.
That cuts development effort considerably: an MCP server built once can be used by different AI applications, and an AI application can connect to numerous servers with little extra effort. That encourages interoperability, speeds up development and eases maintenance.
What is MCP used for in practice?
MCP is used wherever a Language model or an AI agent is to interact with the real world. Examples are access to internal knowledge bases, querying company data, controlling business applications or connecting to development and project management tools.
MCP matters greatly for AI agents in particular, because they depend on external tools and current data to do their work. Through MCP servers they get structured, controlled access to exactly the functions they need, without a bespoke solution having to be built for every connection.
What should companies keep in mind with MCP?
Access to external systems brings security and governance requirements with it. It has to be clearly defined which tools and data an AI host may use through MCP and which permissions apply. Well-considered rights management and logging are therefore indispensable.
Since the ecosystem around MCP is developing fast, expert support in selecting, building and securing suitable MCP servers pays off. Elisabit helps companies connect their AI applications to the right systems through MCP safely and efficiently.
Frequently asked questions
What does the abbreviation MCP stand for?
MCP stands for Model Context Protocol. It is an open standard for connecting AI applications to external tools and data sources. The standard was originally introduced by Anthropic.
What is the difference between an MCP host and an MCP server?
The MCP host, or MCP client, is the AI application itself, such as an assistant or agent. The MCP server provides functions, data and templates the host accesses. The two communicate through the standardised protocol.
Which building blocks can an MCP server provide?
An MCP server can offer tools (executable functions), resources (readable data and documents) and prompts (predefined templates). The host discovers these building blocks and uses them as needed. The language model thus gets structured access to external capabilities.
Why is MCP so important for AI agents?
AI agents need access to external tools and current data for their tasks. MCP provides a uniform interface for this, so no bespoke solution has to be built for every connection. That makes agents more flexible and quicker to deploy.
Related terms
An AI system that pursues goals on its own: perceiving, planning, using tools and acting across several steps.
An AI paradigm that acts autonomously, with a goal in mind and across several steps, instead of merely answering single prompts.
RAG combines a language model with the retrieval of relevant information from external knowledge sources before answering.
An LLM is an AI language model that understands and produces text by predicting the most likely next word.
AI automation uses LLMs and agents to automate even unstructured business processes end to end.
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