Glossary:MCP Server

What is an MCP Server?

An MCP Server (Model Context Protocol Server) is a standardized software component that provides AI systems with structured access to external data, tools, and services. The underlying Model Context Protocol (MCP) was released by Anthropic in November 2024 and transitioned to the Linux Foundation AI & Data Foundation (AAIF) in December 2025 — with OpenAI, AWS, Google, Microsoft, Bloomberg, and Cloudflare as Platinum members. What began as an Anthropic initiative is today the industry-wide communication standard for AI integrations.

The simplest analogy: MCP is the USB-C port of the AI world. Any device that supports the standard can be plugged in and used immediately — without adapters, without specific drivers, without custom programming for every new combination. An MCP Server provides data and capabilities; an MCP Client — such as an AI chatbot — connects to it and can use these resources directly.

By March 2026, the MCP SDK records 97 million monthly downloads, with over 10,000 active MCP Servers and more than 300 MCP Clients worldwide. 54% of MCP developers already consider the standard to be an established industry standard in their sector (Source: Zuplo State of MCP Report, January 2026).

How does an MCP Server work?

MCP is based on a client-server architecture with JSON-RPC 2.0 as the communication protocol. Three roles work together:

1. MCP Host: The AI application the user interacts with — such as an AI chatbot on a website, an enterprise AI assistant, or an autonomous agent.

2. MCP Client: The component within the host that manages connections to MCP Servers, translates requests, and returns responses.

3. MCP Server: A specialized backend service that provides defined resources: Tools (functions the AI can execute), Resources (structured data the AI can retrieve), and Prompts (predefined instruction templates).

The typical workflow of an MCP interaction:

• Capability Discovery: When establishing a connection, the host and server exchange their capabilities. The AI client then knows exactly which tools and resources the server offers.

• Request: The AI system determines that it requires external data and sends a structured request to the server via the MCP Client.

• Execution: The MCP Server executes the action — database query, API call, calculation — and returns the typed result.

• Integration: The AI host processes the result and integrates it into its response.

Crucial to this is type safety: MCP transmits data with clear type definitions. Before processing, the AI client knows exactly what format the incoming data is in — a structural advantage over free-form API responses where the model first has to interpret the format.

The N×M Problem: Why MCP Servers Exist

Before MCP, every company faced the same integration problem: To connect N different AI systems with M different data sources, N×M individual interfaces had to be programmed, maintained, and updated whenever changes occurred.

MCP reduces this to N+M: A data source builds an MCP Server. Any AI system that supports MCP can use this server immediately — without new integration code. If the company changes the AI model, the connection remains intact. If a new data source is added, it is registered as a new MCP Server and is simultaneously available to all AI systems.

58% of MCP developers primarily use MCP to wrap existing APIs and make them accessible to AI (Source: Zuplo State of MCP Report, January 2026).

MCP Server vs. Traditional API Integration

Why MCP Servers are Important for European Companies

An Industry Standard with Strong Tailwind

Handing MCP over to the Linux Foundation is not a technical detail, but a strategic signal: The entire AI industry is agreeing on a common language for tool integration. Much like HTTP unified the web, MCP standardizes how AI systems access external data and capabilities.

According to Gartner, by the end of 2026, 40% of all enterprise applications will have embedded AI agents — compared to 5% in 2025. The market for agentic AI is growing from USD 7.29 billion (2025) to an estimated USD 139 billion by 2034 (CAGR 40.5%, Source: Fortune Business Insights, November 2025). AI agents operating in this infrastructure communicate via MCP. Companies that make their data MCP-compatible now are prepared for this evolution.

Control, Compliance, and Data Protection

European companies operate under GDPR, the EU AI Act, and — for many — the BFSG (European Accessibility Act). Here, MCP provides structured answers to central compliance questions: Who has access to which data? What actions is the AI allowed to perform? Can data paths be audited?

Because MCP Servers define exactly which tools they offer and which data is allowed to flow, access rights and data flows can be precisely controlled and logged — a prerequisite for operating under EU AI Act Art. 9 (Risk management system) and Art. 13 (Transparency).

Proprietary Data as a Competitive Advantage

The value of an AI system depends directly on what data it knows. An AI chatbot that only relies on general training knowledge gives generic answers. One that accesses the current product catalog, stock levels, prices, and booking availability in real time via an MCP Server is a true digital consultant. The MCP Server is the bridge between the trained model and live enterprise knowledge.

branchly: MCP Client and MCP Server in One

branchly plays a dual role in the MCP ecosystem — and that is not a coincidence, but an architectural decision.

branchly as an MCP Client: External Data in the AI Session

The branchlyAI Engine acts as an MCP Client. Customers can store their own MCP Servers in the dashboard, through which branchly accesses internal enterprise data in real time. branchly's AI chatbots and AI search modules retrieve this data in a structured manner for every relevant user query — not as static training, but as live access.

Typical external MCP Servers that branchly customers connect to:

• Product catalogs & PIM systems — current products, prices, availability

• Booking and reservation systems (e.g., in tourism) — real-time capacities

• ERP systems — inventory levels, delivery times, order status

• CRM systems — customer history, segmentation, ticket status

• Logistics services like DHL — shipment tracking, delivery status

• Infomax — structured information and knowledge databases

• Venus — tourism data, destination information

The result: The branchly chatbot on a tourism website does not just know what is written on the website — it knows the current events from the DMS, booking availabilities from the reservation system, and local highlights from the Infomax server. In real time, without manual content maintenance effort.

branchly as an MCP Server: Session Data for External Systems

At the same time, branchly operates its own MCP Server — making its own platform accessible to external AI systems and developer tools.

Through the branchly MCP Server, authorized systems can:

• Evaluate sessions — What questions do visitors ask? Which topics dominate? Where does the conversation break off?

• Analyze conversation flows — Structured querying of conversation data for quality assurance and optimization

• Retrieve interaction metrics — Interaction rates, response quality, conversion signals from AI conversations

• Query intent clusters — AI-classified visitor intentions as structured data for marketing analysis

• Configuration management — Programmatic access to prompt configurations, knowledge base settings, and module definitions

This means: branchly data is not just visible in the dashboard — it is machine-readable via MCP and can be integrated into external BI systems, CRM platforms, data warehouses, and AI agents. This allows marketing teams to seamlessly incorporate visitor intentions from branchly sessions directly into their analytics infrastructure — without manual exports, without proprietary database queries.

MCP Servers in Practice: Typical Use Cases

E-Commerce

An online shop with 50,000 SKUs has daily changing availabilities, promotional prices, and new products. Instead of feeding the AI chatbot with new training data monthly, the shop provides an MCP Server that exposes product data in a structured way. branchlyAI as an MCP Client retrieves this data live during every conversation.

A visitor asks: "Is this model still available in size M in dark blue, and when will it be delivered?" The chatbot queries the MCP Server of the inventory management system, retrieves stock levels and DHL delivery times, and responds precisely — without anyone having to maintain content or update training data.

Tourism

A destination operates a booking system, an events calendar (Infomax), and a content database (Venus). All three are available as MCP Servers. branchly autonomously combines the relevant sources for each visitor query: current booking availabilities, upcoming events, local recommendations — in the visitor's language, without the website itself having to be multilingual.

Logistics and Fulfillment

A shipping service provider integrates the DHL MCP Server. AI agents can query shipment statuses, initiate returns, and update delivery addresses via branchly — without employee intervention. In this scenario, the AI does not act as a passive information provider, but as an active process partner.

Financial Services

A bank exposes its product conditions, tariff structures, and regulatory disclosures via an MCP Server — with clear access rules: What data can the chatbot output? What remains internal? The MCP layer controls this governance centrally. The chatbot automatically forwards sensitive requests that require human advice.

Why MCP Servers are Superior to Proprietary Integrations

The alternative to MCP is direct API integration: A custom interface must be programmed, maintained, and updated for every combination of AI system and data source whenever APIs change. This costs development time, creates technical debt, and locks you into specific AI providers.

MCP solves this structurally:

• Vendor Independence: Changing the AI model requires no new integration. The MCP Server remains unchanged.

• Reusability: An MCP Server built for the ERP system is immediately available to all MCP-compatible AI applications — chatbot, internal agents, analytics tools.

• Auditability: All access goes through the MCP layer and can be centrally logged — a compliance requirement often treated as an afterthought in direct API integrations.

• Standardized Governance: Access rights, rate limiting, and data filter configurations are defined on the MCP Server — not scattered across different AI systems.

Related Terms

• MCPs vs. CLI

• Programmatic Tool Calling

• Agentic RAG

• Agentic Commerce

• AI Chatbot

• RAG (Retrieval-Augmented Generation)

• Hybrid Search