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Enterprise AI Architecture Glossary

What is MCP Server?

Definition

An MCP (Model Context Protocol) Server is a standardized, open-source application layer that securely connects AI models to external data sources, internal APIs, and local files.

How Memora Leverages MCP Server

Memora uses MCP-compatible integrations to connect organizational data sources with its memory and retrieval systems.

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claude_desktop_config.json
{
  "mcpServers": {
    "memora-memory": {
      "command": "npx",
      "args": [
        "-y",
        "@memora/mcp-server@latest"
      ],
      "env": {
        "MEMORA_API_KEY": "YOUR_MEMORA_API_KEY",
        "MEMORA_WORKSPACE_ID": "ws_enterprise_main"
      }
    }
  }
}
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What Is an MCP Server? Complete Guide

When organizations attempt to integrate Artificial Intelligence into their daily workflows, they hit an immediate roadblock: AI models don't naturally know how to read your private Jira tickets, query your secure PostgreSQL database, or understand your local GitHub repository.

Enter the MCP Server.

An MCP (Model Context Protocol) Server is a lightweight, standardized application layer that exposes specific local data sources, file systems, and internal APIs to an AI model. In this complete guide, we'll explain how MCP servers work, why MCP is becoming a standard way for AI applications to interact with tools and data sources, and how they act as the foundation for modern AI Agents.

What is an MCP Server?

An MCP Server is essentially a data provider in the Model Context Protocol architecture. It acts as a universal adapter between your private data and an AI application.

Think of it like a USB-C cable for AI. Before USB-C, every device required a proprietary charger. Before MCP, every AI model required custom, hardcoded integrations to talk to different databases or SaaS tools. An MCP server standardizes this connection: the developer writes the server logic once, and any AI client that speaks the Model Context Protocol can instantly understand and interact with that data.

MCP Server vs MCP Client

The Model Context Protocol architecture is divided into distinct roles:

ComponentRole
MCP HostThe AI application or environment running the agent.
MCP ClientMaintains the connection to an MCP server on behalf of the host.
MCP ServerExposes tools, resources, and prompts to the client.

How Does an MCP Server Work?

The Model Context Protocol operates on a strict client-server architecture.

  1. The Client (AI Host): The application running the AI model initiates a connection.
  2. The Server (Data Provider): The MCP server listens for requests, translates them into local actions (like a SQL query or a file read), and returns the results to the AI.

Communication commonly uses stdio (standard input/output) for local, process-spawned integrations and Streamable HTTP for remote MCP servers. Legacy HTTP+SSE remains supported for backwards compatibility, but the MCP project has deprecated it in favor of Streamable HTTP.

MCP Server Architecture

An MCP server exposes three core primitives to the AI client:

  1. Resources: Read-only data. An MCP server can expose a specific configuration file, a list of Slack messages, or database schemas. The AI reads this context to understand its environment.
  2. Tools: Executable actions. Tools are functions the AI can trigger. For example, an MCP server might provide a tool called execute_sql_query. The AI decides to call this tool, the server executes it, and returns the result.
  3. Prompts: Pre-written templates that the MCP server provides to the AI to ensure it formats queries or handles the specific data source correctly.

MCP Server vs API: What's the Difference?

While both MCP servers and standard REST APIs connect systems together, they are built with different consumers in mind.

  • REST APIs provide general-purpose programmatic interfaces. They require hardcoded integration, rigid JSON schemas, and manual error handling by human developers.
  • MCP provides a standardized interface specifically designed for AI applications to discover and interact with tools, resources, and prompts.

The client can discover the server's available capabilities and then decide which tools or resources are relevant to the task.

MCP Server vs RAG

While they both help AI models access data, they are fundamentally different:

  • MCP is a standardized access/interface protocol. It dictates how the AI communicates with a system.
  • RAG (Retrieval-Augmented Generation) is a retrieval and generation approach. It dictates what data is pulled and injected into the prompt.

They can work together seamlessly. An AI might use an MCP Server to securely query a vector database (performing RAG) to pull relevant enterprise context.

What Is an MCP Server Used For?

By acting as the hands and eyes of an AI, MCP servers unlock powerful enterprise use cases, including:

  • Connecting AI directly to secure databases.
  • Accessing local files and codebases securely.
  • Interacting dynamically with SaaS tools (like GitHub or Jira).
  • Giving autonomous agents controlled tools to execute approved actions.
  • Retrieving enterprise context to build Organizational Memory.

MCP Server Examples

The open-source community has rapidly built a massive ecosystem of MCP servers. Some popular examples include:

  • PostgreSQL MCP Server: Allows an AI to read schemas and execute read-only queries against a private database.
  • GitHub MCP Server: Lets an AI read repositories, review pull requests, and create issues.
  • Google Drive MCP Server: Connects an AI to a corporate Drive to search and summarize documents.

How to Build an MCP Server

Building an MCP Server is surprisingly straightforward, especially using official SDKs available in TypeScript and Python.

At a high level, the process involves:

  1. Importing the MCP SDK.
  2. Defining a Server instance.
  3. Registering Tools (e.g., server.tool("read_file", ...)).
  4. Registering Resources and Prompts.
  5. Starting the server via the stdio or Streamable HTTP transport layer.

MCP Servers for AI Agents

We are moving past simple chat interfaces into the era of autonomous AI Agents. An agent needs to perceive its environment and take action.

MCP servers are the hands and eyes of these agents. Instead of giving an agent broad, dangerous access to the internet, you confine the agent to a specific set of MCP servers. The agent uses the MCP server's tools to navigate the environment, achieving its goals securely.

MCP Server Security

MCP provides a standardized interface, but security still depends on how the server, client, authentication, authorization, and underlying data sources are configured.

  • Local Execution Options: An MCP server can run within your own infrastructure, allowing organizations to keep data access and processing within controlled environments depending on their architecture.
  • Granular Permissions: You strictly define what the AI can do. If you only expose a read_ticket tool, it is impossible for the AI to accidentally delete a ticket.
  • Human-in-the-Loop: MCP clients can be configured to prompt a human for approval before the MCP server executes a sensitive tool.

Frequently Asked Questions (FAQ)

Do I need to be a developer to use an MCP Server?

Deploying and configuring a custom MCP Server requires development knowledge (TypeScript or Python). However, using an AI that connects to pre-built enterprise MCP Servers requires no technical skills from the end-user.

Does an MCP Server train the AI model on my data?

No. The MCP Server only provides context to the model at inference time. It does not alter the underlying foundational model or use your private data for training.

What communication protocols do MCP Servers use?

MCP supports stdio for local, process-spawned integrations and Streamable HTTP for remote deployments. Legacy HTTP+SSE is retained for backwards compatibility but has been deprecated in the current MCP specification.

Is MCP tied to a specific AI vendor?

No, it is an open-source standard. While initially championed by Anthropic, the protocol is designed to be vendor-agnostic and is rapidly being adopted across the entire AI industry.

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