AI models are incredibly powerful, but historically they’ve been isolated from the rich data and tools that organizations use every day. Every new integration – whether connecting a model to a database, a document repository, or an API – required a custom solution.

Introduction

AI models are incredibly powerful, but historically they’ve been isolated from the rich data and tools that organizations use every day. Every new integration – whether connecting a model to a database, a document repository, or an API – required a custom solution. This fragmentation has made it difficult to scale AI systems across diverse contexts​. To tackle this challenge, Anthropic introduced the Model Context Protocol (MCP) in late 2024 as an open standard for bridging AI assistants with the systems where data lives​. Anthropic describes MCP as “a USB-C port for AI applications” – a single, standardized way to plug language models into various data sources and tools​. In this post, we’ll explore what MCP is, how it works under the hood, and why it matters for developers building the next generation of AI-powered applications.

What is the Model Context Protocol (MCP)?

At its core, MCP is an open protocol that standardizes how applications provide context to large language models (LLMs)​. The goal of MCP is to replace today’s patchwork of one-off integrations with a universal interface that any AI assistant and data source can share. In practical terms, MCP enables developers to build secure, two-way connections between AI models and external resources like databases, file systems, APIs, or business applications​. Instead of writing custom code for each data source or API an AI needs to access, a developer can implement MCP once and gain a consistent “plug-and-play” connection. This means an AI assistant can draw on relevant information or execute operations in other systems seamlessly through the MCP interface.

Why was MCP needed? As AI assistants gained adoption, even the most sophisticated models were “trapped” behind information silos – they couldn’t easily fetch data from a company’s knowledge bases or take actions in software tools​. Developers had been integrating models with data sources in an ad-hoc way, which was hard to maintain and scale. MCP addresses this by providing one standard protocol for connecting AI to data, eliminating the need for separate bespoke connectors for each tool​. In Anthropic’s words, *“MCP provides a universal, open standard for connecting AI systems with data sources, replacing fragmented integrations with a single protocol.”*​ By open-sourcing MCP, Anthropic also invites a community effort – anyone can build MCP-compatible integrations, leading to a growing ecosystem of connectors.

Key benefits of MCP include:

Key Benefit	Description
Plug-in Integrations	A growing library of pre-built MCP connectors for popular systems (e.g., Google Drive, Slack, GitHub, databases). Developers can reuse these instead of building from scratch.
Vendor Flexibility	MCP is model-agnostic and serves as a layer between applications and LLMs. It allows switching between different LLM providers or models without reworking integrations.
Security by Design	Encourages best practices for secure data access. Data stays within your infrastructure, and MCP includes capability negotiations and user permissioning.

In short, MCP’s purpose is to make it easier to build context-aware AI applications. Early adopters like Block and Apollo have already integrated MCP into their systems, and developer-tool companies like Zed, Replit, Codeium, and Sourcegraph are working with MCP to enhance their platforms – enabling AI agents to fetch relevant information and produce more relevant, functional outputs with fewer trial-and-error cycles​. Anthropic has even open-sourced a suite of MCP servers for common enterprise systems (e.g. Google Drive, Slack, Git, PostgreSQL) to jumpstart the ecosystem​. The vision is that as the MCP ecosystem matures, AI systems will be able to maintain context across different tools and datasets effortlessly, rather than being limited by one app at a time​.

MCP Architecture: How It Connects AI to Tools

Under the hood, the Model Context Protocol follows a client–server architecture to link AI applications with external integrations​. Here’s the basic anatomy of MCP’s architecture:

Component	Role
MCP Host (LLM Application)	The AI-powered application or assistant that wants to use external data/tools (e.g., Claude Desktop, AI coding IDE, custom chat interface). It initiates connections to MCP servers when it starts.
MCP Server (Integration)	A lightweight program exposing a specific data source or capability via the MCP standard (e.g., Google Drive for file access, Postgres for database queries). Each server focuses on one domain and runs independently.
MCP Client (Connector)	Lives inside the host application. Maintains a 1:1 connection with each MCP server, communicates using the MCP protocol, and relays data/results between the host and the server.

 

This design cleanly separates concerns: AI application developers can focus on the host side (how to use the data in model prompts or tool calls), while integration developers focus on the server side (how to fetch data from, say, Slack or Salesforce). They meet at the MCP interface. This separation is similar to how microservices decouple different functionalities in enterprise software, or how the Language Server Protocol (LSP) decouples code editors from programming language specifics. In fact, MCP takes inspiration from Microsoft’s LSP – just as LSP standardized the way editors communicate with language analyzers, MCP standardizes how AI systems communicate with context and tool providers​.

Communication via JSON-RPC

All MCP connections are stateful and follow a defined lifecycle. When a client connects to a server, it first performs an initialization handshake where each side exchanges its protocol version and advertised capabilities​. This handshake ensures that both client and server agree on what features they support (for instance, a server will declare if it provides “resources” or “tools” features, and a client will declare if it supports certain optional behaviors like "roots" – a way to scope the server’s access to specific sub-resources). After initialization, the connection enters the message exchange phase, where the host and server freely send requests or one-way notifications to each other​. Either side can terminate the connection gracefully when done (or handle unexpected drops), but typically the MCP servers run continuously and the host maintains the connection as long as the AI assistant is active​.

The MCP protocol is transport-agnostic, meaning it can work over different underlying communication channels. The two main transports supported are:

• Stdio (Standard I/O) Transport: This uses the standard input/output streams of a process for communication​. It’s ideal for local integrations – for example, an MCP host can spawn a server process on the same machine and communicate via pipes. This approach is simple and efficient for same-machine or same-container setups. (It’s analogous to how LSP servers often run as subprocesses communicating via stdio.)
• HTTP + SSE Transport: This mode is suitable for remote or cloud-based servers. It uses HTTP POST requests for the client-to-server direction and Server-Sent Events (SSE) for server-to-client streaming messages​. In practice, the host would POST requests to an HTTP endpoint where the MCP server is hosted, and the server would push responses or notifications back via an SSE stream. This allows using MCP over the web with proper authentication, and behind corporate firewalls or cloud functions.

Regardless of transport, every message is still a JSON-RPC payload under the hood​. JSON-RPC defines a few basic message types that MCP leverages: Requests (which expect a response), Responses (with a result or error), and Notifications (one-way messages that expect no reply) ​. For example, when the host wants to get a list of available files from a filesystem server, it will send a request message like this:

{
  "jsonrpc": "2.0",

  "id": 42,

  "method": "resources/list",

  "params": {}
}

And the server, if it can fulfill that, will reply with a corresponding result:

{
  "jsonrpc": "2.0",

  "id": 42,

  "result": {
    "resources": [
      { "uri": "file:///path/to/file1.txt", "name": "file1.txt" },
      { "uri": "file:///path/to/file2.txt", "name": "file2.txt" }
    ]
  }
}

In this hypothetical JSON exchange, the "method": "resources/list" corresponds to an MCP operation for listing available resources, and the server returns a JSON object with a list of resource descriptors (each identified by a URI and maybe a human-friendly name). The id field ties the response to the original request. This JSON-RPC structure makes it easy for any MCP-compliant client and server to understand each other – if a new server type comes along, as long as it uses the standard method names and schemas defined by MCP, existing hosts can interact with it without custom code.

Capabilities and Features

One powerful aspect of MCP is its extensible set of features for what a server can offer to the host (and vice versa). The MCP specification defines a few categories of capabilities that can be mixed and matched depending on what the integration does​:

Feature	Description
Resources	Provides data or content (e.g., documents, files, DB records) as read-only context for the model. Each resource has a unique URI and can be text or binary. Example: A Google Drive MCP server exposing files. Helps answer: "What information is available for the model to use?"
Prompts	Allows the server to supply templated prompts or conversational flows. Useful for guiding the model with predefined workflows or domain-specific structures. Example: Customer support templates for handling queries.
Tools	Represents callable actions/functions exposed via the server (similar to plugins or function calling). The AI sends tool name + parameters, and the server executes it. Example: create event on a calendar server or send message on a Slack server. Enables AI to take controlled actions.
Sampling	Lets the server request the host to generate a model output as part of a larger task. Useful in complex workflows (e.g., research agents). Enables recursive or multi-agent reasoning by offloading intermediate tasks back to the LLM. Note: As of early 2025, not all clients (e.g., Claude) support it yet.

These features are negotiated during the connection handshake. An MCP server will advertise which of these it supports (for example, a purely read-only data server might declare only “resources”, whereas an interactive service might declare both “resources” and “tools”). The client will advertise what it can handle (some lightweight clients might not implement all features). This negotiation means both sides know what to expect. Then the protocol defines specific methods for each feature type. For instance, if a server supports Resources, it will implement methods like “List available resources” and “Read a resource’s content”. If it supports Tools, it will implement methods to list available tools and to invoke a tool. All of these methods are standardized in the MCP spec so that hosts can call them uniformly across different servers.

Model Context Protocol (MCP) in Python

Below is a comprehensive example demonstrating an MCP server that provides text-based resources (short documents), and a Python client that connects via STDIO, retrieves those resources, and uses their content in a prompt to the OpenAI API. We use the official MCP Python SDK (which supports building MCP servers/clients and STDIO transport​) and the OpenAI Python SDK for the model call. The example is structured with separate server and client code, includes commentary, and follows best practices for error handling and logging.

Example Overview

1. MCP Server (server.py) – Implements the MCP 'resources' capability by serving one or more text documents. We use FastMCP from the MCP SDK to define resource URIs and their retrieval logic.
2. MCP Client (client.py) – Uses the MCP SDK to launch the server (via STDIO) and interact with it. The client lists available resources, reads a resource’s content, and then constructs a prompt with that content as context. This prompt is sent to the OpenAI API (e.g. GPT-4) using the openai SDK, and the model’s response is printed out.
3. OpenAI API Integration – We form a chat completion request including the retrieved resource content (demonstrating a RAG flow). The OpenAI API key is assumed to be configured in an environment variable for local use.