(oai) Model Context Protocol (MCP) – A Technical Overview

5/2/25

Introduction and Background

The Model Context Protocol (MCP) is an open standard for connecting AI/ML models (especially large language models) to the external data sources, tools, and environments they need for richer context. MCP was introduced by Anthropic in late 2024 as a response to the “M×N integration problem” in AI applications. Before MCP, each AI application had to integrate with each data source via a bespoke API or plugin, leading to a proliferation of one-off connectors. This is analogous to the pre-USB era where every peripheral required a custom port/driver. MCP aims to simplify this by providing a universal “USB-C”-like interface for AI – turning the integration problem from M×N (each of M models to each of N tools) into M+N, where each tool is written once as an MCP server and each AI app implements an MCP client. In short, MCP standardizes how applications supply context and capabilities to LLMs, making it easier to plug a model into many systems or swap models without rewriting all integrations.

Before MCP vs After MCP: On the left, an LLM must use a unique integration for each service (Slack, Google Drive, GitHub, etc.). On the right, MCP provides a unified interface between the LLM and various services. This consolidation reduces fragmented, custom integrations in favor of one standard protocol.

Why MCP? Large language models are powerful but “trapped” without access to relevant external data. By default, an LLM only knows what’s in its prompt or training data. MCP addresses this by enabling secure, real-time access to current information, company content, tools (like databases or APIs), and other context during an AI interaction. Developers and organizations had been struggling with ad-hoc solutions – e.g. writing custom code to pull data for each query or using proprietary plugin systems. MCP provides a standard, open protocol so that any AI assistant or agent can connect to any data/tool connector implementing MCP. This fosters an ecosystem where improvements to one connector benefit all AI apps, and new models or applications can readily leverage existing connectors. Anthropic and others compare MCP’s potential impact to that of other unifying tech standards: for example, how ODBC standardized database access or how Language Server Protocol (LSP) unified IDE integrations. The vision is that hooking up a new data source for an AI would become “as simple as plugging in a USB device – no custom code or prompts required”.

MCP Architecture and Components

At its core, MCP follows a client–server architecture within the AI application environment. The main roles are: Hosts, Clients, and Servers:

Each MCP server can provide up to three categories of capabilities, as defined by the protocol:

Additionally, MCP supports an advanced feature on the client side called Sampling, which allows a server to request the host to perform an LLM generation on its behalf (a form of server-initiated AI action). This can enable more complex two-way workflows. For example, a server might say “I have fetched some raw data; please summarize it with the model and then return that summary to me.” Such interactions are carefully controlled and require user permission, but they illustrate that MCP is not just one-way – it is a bidirectional protocol.

General Architecture: The diagram below illustrates how these components interact. The host (with an LLM) runs an MCP client that connects to an external MCP server. The server exposes tools, resources, and prompts, and interacts with underlying systems (APIs, databases, etc.) as needed. Communication between client and server is handled via a transport layer carrying JSON-RPC messages (requests, responses, notifications) over a persistent connection.

High-level MCP architecture (client–server). The Host contains the LLM and an MCP client. The MCP Server provides Tools, Resources, and Prompts to the AI. Under the hood, JSON-RPC messages (over a transport like stdio or SSE) carry requests and responses between client and server. The server then invokes the actual external services (e.g. Weather API, Email API, Database) to fulfill requests.

Communication and Protocol: MCP uses JSON-RPC 2.0 as the message format for all exchanges. This means every action (like “list tools” or “call this function”) is a JSON message with a method name and params, and results come back as JSON responses. The protocol is stateful – the client and server maintain a session, allowing multi-step interactions and context to persist across requests. This is in contrast to typical REST APIs which are stateless request/response pairs. MCP’s stateful connection enables richer interactions: for example, the server can stream back incremental results or ask follow-up queries to the host. JSON-RPC also supports notifications (messages without a response) which MCP uses for things like progress updates or cancellations.

Transports: How do the client and server actually exchange these JSON-RPC messages? MCP is transport-agnostic, but the standard defines two main transport mechanisms out-of-the-box: Standard I/O and Server-Sent Events (SSE).

Security Considerations: Because MCP gives AI systems access to potentially sensitive data and powerful tools, the protocol comes with guidance for security and trust. MCP is designed such that user consent and control are paramount – e.g. users must authorize which data an AI can access and approve any actions like sending an email. Hosts should enforce data privacy (not exposing data to servers without permission) and clearly prompt users before an AI invokes a tool that can have side effects. For instance, Claude’s implementation requires the user to approve each tool use. This ensures an AI agent can’t, say, delete files or leak info unless explicitly allowed. The MCP spec also notes standard secure coding practices for connector developers (to avoid exploits like DNS rebinding in local servers, etc.). In short, MCP introduces a controlled integration layer: all data access and operations are brokered through the MCP client, which can log and monitor them, making it easier to audit AI tool usage in one place.

How MCP Works: Interaction Flow

With the architecture in mind, here’s a typical sequence of how an AI system would use MCP in practice – from startup to answering a user’s query:

  1. Initialization: When the host application starts (or when a new session begins), it launches or connects to the required MCP servers. For each server, an MCP client instance is created. The client and server perform a handshake to exchange version info and negotiate capabilities (this ensures both sides know what features each supports, e.g. which of Tools/Resources/Prompts are provided).
  2. Discovery: After handshake, the host (via the client) queries the server for its available capabilities. The MCP server replies with a list of the tools, resources, and prompts it offers, along with descriptions and input/output schemas. This is analogous to discovering an API’s endpoints or a plugin’s functions. The host can log or display this info if needed.
  3. Context Provisioning: The host can now make use of the provided context. For example, it might fetch certain resources upfront to show the user (e.g. “customer profile data” to load into the chat context), or prepare the LLM by incorporating a relevant prompt template from the server. Additionally, the host may translate the available tools into a format the LLM can understand – for instance, if using OpenAI’s function calling, the host can create a JSON function schema for each tool so the LLM knows it can call send_email(to, body) etc.. At this stage, the user interface might also present new options (e.g. showing that a Salesforce connector is attached and can be queried).
  4. Invocation (Runtime): When the AI is generating a response and decides it needs external info or to perform an action, it triggers an MCP tool invocation or resource fetch. In practice, the LLM might output a special token or function call (if using function-calling API) indicating which tool to use and with what arguments (e.g. it might “ask” for fetch_github_issues(repo="X")). The host intercepts this and directs the appropriate MCP client to send the request to the server. (If not using an automatic function-calling model, the host could decide based on the user request – e.g. a user asking “What are the open issues in repo X?” could be routed by the host to call the GitHub server’s tool directly.)
  5. Execution: Upon receiving the request, the MCP server executes the underlying logic. This could mean calling an external API (like GitHub REST API in this example), running a database query, reading a file, or any action needed to fulfill the request. The server then gathers the result (e.g. a list of issues from GitHub).
  6. Response: The server sends the result back to the client as a JSON-RPC response message. The MCP client receives it and passes it to the host application. This result could be data (for a resource query or tool call) or simply a confirmation that an action succeeded. The protocol allows streaming partial results as well (for example, streaming lines of a file or incremental updates).
  7. Completion: Finally, the host takes the server’s result and incorporates it into the LLM’s context. In our example, the host might insert the list of GitHub issues into the conversation (perhaps as part of the assistant’s reply or as a supplemental attachment) so that the LLM can refer to it when formulating the answer. The LLM then produces the final answer to the user, now enriched by the external data. From the user’s perspective, the AI assistant has “knowledge” of the latest GitHub issues. This loop can repeat if the AI needs more information or to perform another action in the course of the conversation.

Throughout this flow, the user remains in control: they could be shown what tools the AI plans to use and approve them, and they ultimately see the augmented answer. The key is that MCP provides a coherent framework for this entire interaction – from discovering capabilities to invoking them and injecting results back into the model’s prompt. Without MCP, a developer would have to wire up each of these steps manually (and differently for each data source), whereas MCP standardizes the pattern.

Use Cases and Example Scenarios

MCP is designed to be broadly applicable anywhere AI assistants or agents need to interface with external systems. Some concrete use cases include:

Notably, many companies are rapidly adding MCP support. Connectors already exist (officially or via community) for services like GitHub, Slack, Google Drive, Gmail, Notion, databases (PostgreSQL), web browsers (Puppeteer), and more. As one analyst observed, this is creating a “platform opportunity akin to an App Store” for AI tools – a growing library of MCP servers that can be plugged into any AI client. This standardization also simplifies governance: an organization can monitor and manage all AI interactions with external systems via the MCP layer (e.g. logging all tool calls), rather than chasing down numerous custom integration logs.

Of course, MCP is not a silver bullet for every scenario. For very simple integrations (say your app just needs to call one or two well-documented APIs), adding MCP might be overhead compared to a direct API call. But as the number of integrations grows, MCP’s benefits compound – especially in enterprise environments or complex products where consistency and scale matter.

MCP vs Traditional APIs and Middleware

How does MCP differ from using normal APIs or integration middleware? The table below summarizes the key differences in purpose, function, and integration model between MCP, traditional APIs, and generic middleware:

Aspect **Model Context Protocol (MCP)** **Traditional APIs** **Traditional Middleware**
**Primary Purpose** Standardize how AI applications access external data & tools as context. MCP is purpose-built to feed information *into* LLMs and allow LLMs to invoke actions in a controlled way. Expose a software system’s functionality or data for any client (not specific to AI) via a contract (REST, RPC, etc.). Each API is specific to one service (e.g. Google Maps API for maps). Integrate disparate systems or components by routing, transforming, and orchestrating data flows. Middleware (e.g. enterprise service buses, message queues) mediates between systems at scale, often for reliability or complexity management.
**Integration Model** **Client–Server plugin architecture**: AI apps dynamically load “connectors.” The LLM client discovers tools/resources at runtime and calls them via a uniform protocol. Emphasizes *runtime flexibility* – new tools can be added without code changes to the AI app (just add a new MCP server). **Design-time API calls**: Developers integrate by writing code against each API’s spec (often using SDKs). The set of called APIs is typically fixed at design/deploy time; adding a new API usually means deploying new code. **Configured or coded flows**: Middleware often requires upfront configuration or pipelines (e.g. setting up message topics, transformation rules, or using an integration platform). Changes might need redeploying configurations or new adapters, though some ESBs allow dynamic plugins.
**Communication** **JSON-RPC over stateful connection**: The AI host maintains a session with each MCP server. Supports bidirectional comm – e.g. server can push data (stream results or request model actions). Context (like loaded resources) can persist across multiple calls. **Typically stateless requests**: Most web APIs use HTTP request/response (REST) which is stateless. Each call is independent (no built-in session memory, though cookies or tokens might maintain some state server-side). Some APIs (GraphQL subscriptions, WebSockets) allow streaming or push, but it’s not universal. **Varied (often stateful)**: Middleware might use message queues, persistent connections, or event streams. Many middleware systems maintain state or session in the form of message state, but it’s not standardized across all. E.g. an MQ broker keeps a session for a subscriber. The key difference: middleware isn’t a single protocol; each system (Kafka, IBM MQ, etc.) has its own.
**Data/Context Handling** **Designed for LLM context**: MCP directly returns data in a format suitable for LLMs (text or structured data that the host can feed into a prompt). It not only executes commands but also helps *inject the results into the model’s context*. Also supports *prompts* as first-class items. **Raw data focus**: APIs give you raw data or perform actions, but they don’t concern themselves with how an AI will use that data. Any integration into an LLM’s context (e.g. formatting JSON into a prompt) is up to the developer. There’s no concept of “prompt templates” in an API. **Data transformation**: Middleware might transform or enrich data, but it’s not specific to LLM usage. It’s about connecting systems (e.g. syncing a database update to a CRM). The developer still must decide how to incorporate the data into an AI workflow if needed.
**Standardization Level** **Highly standardized and neutral**: MCP defines a single schema for describing tools and data (like a unified API spec for any integration). Any client can talk to any server. It abstracts underlying APIs – e.g. a Slack MCP server might internally use Slack Web API, but the AI sees just `send_message`. This neutrality means you can switch out vendors or data sources behind the scenes as long as they have an MCP server. **Standard per API, not across them**: Each API may follow REST/JSON conventions, but endpoints and auth differ. OpenAPI provides a standard *description* format for APIs, but each API still has a unique interface. There’s no uniform way to call two different APIs without learning each one. Some meta-APIs exist (e.g. GraphQL as a unified interface for several services), but those are custom setups per project. **Standard within a platform**: Middleware often imposes a company-specific standard (e.g. all services must talk via a certain message schema or use a specific bus protocol). There are standards like JMS for messaging or ODBC/JDBC for databases that unify classes of systems. MCP is somewhat analogous to those, but specifically for AI context integration. Traditional middleware doesn’t provide a universal standard for “any external tool” in the way MCP aspires to.
**Dynamic Discovery** **Yes – discoverable**: MCP allows clients to programmatically list available tools, resources, and prompts at runtime. The AI app can adapt to whatever the server provides. This enables *late binding*: you can plug in a new MCP server and the AI will know its capabilities via discovery calls. **Typically no**: With REST/HTTP APIs, you need prior knowledge (or read an OpenAPI spec offline). Some systems have discovery (e.g. a service registry or GraphQL introspection), but it’s not universal and not aimed at AI usage. **Varies**: Some middleware (like SOAP with WSDL, or certain enterprise registries) support service discovery, but it’s often enterprise-specific. Middleware is usually configured with knowledge of endpoints ahead of time.
**Integration Breadth** **One-to-many via connectors**: One MCP client (host) can connect to many MCP servers concurrently, and handle them uniformly. Likewise, a given MCP server can be used by many different AI hosts. This decoupling maximizes reuse – e.g. a community-built Google Maps MCP server could be used in any number of chatbot apps. **One-to-one or few-to-few**: An API is typically used by many clients, but each client integrates each API separately. There’s no notion of a single integration point giving access to multiple services (except aggregators, which themselves are custom APIs). Developers often rewrite similar integration logic for each new app. **Many-to-many but custom**: Middleware can connect many systems, but usually within a specific integration solution. It doesn’t automatically make a new data source available to all apps unless they explicitly use that middleware. Reuse comes from corporate standards (e.g. every app uses the ESB), but outside that context, it’s not portable.
**Example** AI assistant wanting to check inventory and place an order: It uses an Inventory MCP server (resource: `item_stock`, tool: `reserve_item`) and an Orders MCP server (tool: `create_order`). The AI seamlessly calls these without knowing the API details, and the results are inserted into its response. The same assistant without MCP: the developer must call the inventory REST API (e.g. `GET /api/item/stock?id=123`) and the order API (`POST /api/orders`) via custom code, then feed the results into the prompt. The AI itself can’t call the API directly; it relies on the app’s code. A message broker approach: the assistant’s request goes onto a queue, a separate service picks it up and queries inventory, then places an order, then returns a message. This might work but is a much more complex asynchronous pipeline, not a direct interactive query. Also, the AI developer must handle the messaging logic, which is outside the model’s context loop.

In summary, MCP is complementary to traditional APIs, not a replacement. It builds atop existing APIs to provide an AI-friendly layer. You can think of MCP as a specialized middleware for AI: it maintains context, uses a standardized schema, and operates at the level of “tools and data for AI” rather than low-level web requests. One concrete comparison made by observers is that OpenAPI (Swagger) is a standard to describe how to call an API (for human developers), whereas MCP is a standard for an AI agent to actually call and use an API. In fact, tools are emerging to auto-generate MCP servers from OpenAPI specs – meaning if you already have an API, you can wrap it in an MCP interface so that an AI can use it more directly. Traditional middleware aims to connect systems reliably in the background, whereas MCP’s focus is on real-time, on-demand integration of external knowledge into AI’s thought process.

Another way to frame it: with traditional approaches, an AI application’s integration logic is often hard-coded or tied to a specific platform (e.g. ChatGPT plugins, which require a hosting in OpenAI’s environment and a provided OpenAPI spec). MCP instead proposes a vendor-neutral, runtime-pluggable integration model. This allows, for example, a company to switch their LLM provider (say from one vendor’s model to another) or use multiple different LLMs, and still use the same MCP connectors for data – the interface remains consistent. Likewise, tool providers (like Slack or Stripe) could maintain a single MCP connector that any AI client can use, rather than writing separate plugins for each AI platform. This universality and decoupling of components is what makes MCP a significant innovation in the AI/ML systems landscape.

Example Implementations and Pseudo-Code

To make the concepts more concrete, let’s look at how one might implement and use MCP in an AI stack. We’ll consider two sides: creating an MCP server (for a new tool or data source) and integrating an MCP client into an AI application. These examples are in Python pseudocode for illustration.

Implementing an MCP Server (Connector)

Suppose we want to create a simple MCP server that offers a few capabilities: a calculation tool, a greeting resource, and a prompt template for code review. Using an MCP server framework (such as the open-source FastMCP in Python), we could do something like this:

from fastmcp import FastMCP

# Initialize an MCP server for our domain (name "Demo")
mcp = FastMCP("Demo")

# Define a Tool: a function the model can call to add two numbers
@mcp.tool()
def add(a: int, b: int) -> int:
    """Add two numbers"""
    return a + b

# Define a Resource: a read-only data source (personalized greeting text)
@mcp.resource("greeting://{name}")
def get_greeting(name: str) -> str:
    """Get a personalized greeting"""
    return f"Hello, {name}!"

# Define a Prompt: a template to guide the model in a code review task
@mcp.prompt()
def review_code(code: str) -> str:
    return f"Please review this code:\n\n{code}"

In this example, the FastMCP library’s decorators automatically register the functions as MCP capabilities. The add function becomes an exposed tool (the server will advertise a tool named “add” with two integer params). The get_greeting function becomes a resource reachable via a URI scheme (greeting://Alice would return “Hello, Alice!”) – perhaps the host could call this at conversation start to greet the user. The review_code function provides a text template; it’s not meant to be called by the model at runtime, but rather provided to the user or used by the host to preface the model’s output when doing code review. Once implemented, this MCP server can be run as a process (e.g. python demo_server.py) and it will listen (via stdio or SSE) for client connections.

Real-world connectors: Developers have built many MCP servers for common systems. For instance, there are MCP servers for Slack (exposing tools like send_message, resource for channel history), Google Drive (resource to read files, tool to upload files), web browsing (tool to fetch a URL, returning the content), etc. These servers follow the same pattern: wrap the target system’s API and expose a simplified interface for the AI. Many such connectors are openly available, and Anthropic provided reference implementations for Google Drive, Slack, GitHub, Git (repo access), databases (Postgres), and even Puppeteer for web automation. This means if you want your AI agent to have browsing capability, you don’t need to write it from scratch – you can use the existing Browser MCP server and be confident any MCP-compliant AI app can use it.

Using MCP in an AI Application (Client Perspective)

On the AI application side (the Host), using MCP typically involves an MCP client library. For example, if we’re building a Python app that uses an LLM and we want to incorporate the above “Demo” server, we would do something like:

from mcp import ClientSession, StdioServerParameters
from mcp.client.stdio import stdio_client

# Define how to launch/connect to the server (using stdio in this case)
server_params = StdioServerParameters(command="python", args=["demo_server.py"])

# Start the MCP client and session for the server
async with stdio_client(server_params) as (read_stream, write_stream):
    async with ClientSession(read_stream, write_stream) as session:
        await session.initialize()             # Handshake with server

        tools = await session.list_tools()     # Discover available tools
        resources = await session.list_resources()  # Discover resources
        prompts = await session.list_prompts() # Discover prompts

        print("Tools:", tools)       # e.g. ["add"]
        print("Resources:", resources) # e.g. ["greeting://{name}"]
        print("Prompts:", prompts)   # e.g. ["review_code"]

        # Invoke a tool
        result = await session.call_tool("add", {"a": 5, "b": 3})
        print("Result of add:", result)        # e.g. 8

        # Use a resource
        content, mime = await session.read_resource("greeting://Alice")
        print("Greeting for Alice:", content)  # "Hello, Alice!"

Let’s break down what this does. We configure the client to launch the server (demo_server.py) using stdio and then create a ClientSession over the I/O streams. After initialize(), we query what tools, resources, and prompts the server offers (as discussed in the discovery phase). We then programmatically know what this server can do. In this case, tools might list "add", resources might list a pattern like "greeting://{name}", and prompts might list "review_code". The client can now use these. We show an example of calling the “add” tool with arguments, and reading the greeting resource. In a real AI assistant, instead of just printing, the result of call_tool would be fed into the LLM’s next prompt (e.g. “The sum is 8.”) or directly returned if it was answering the user.

To integrate with an LLM, one common approach is to use the model’s function-calling or tool-use mechanism. For instance, with OpenAI GPT-4 function calling, you could register each MCP tool as an available function. When the model chooses to call one, your code (the host) intercepts that function call and simply forwards it via session.call_tool(...) to the MCP server. The result is then inserted back into the conversation for the model to see. This way, the process of the model using a tool is fairly seamless. The host code doesn’t need to know the details of the tool’s implementation – it just passes the request along.

For example, if the user asks “Could you sum 5 and 3 for me?”, the model (knowing the “add” tool is available) might reply with a function call JSON { "function": "add", "arguments": {"a":5,"b":3} }. The host receives this, calls the MCP client which executes the tool, gets 8 back, and then the host gives the model something like: “Tool add returned: 8”. The model then responds to the user: “The sum is 8.” All of this is enabled by the MCP client–server layer; without MCP, the developer would have to write a custom function and code to do the addition (trivial in this case, but for a more complex action like searching GitHub issues, MCP saves a lot of effort).

Multi-Tool Orchestration: If an application has multiple MCP servers (say one for Slack, one for Google Drive, one for Gmail), the host might spin up multiple ClientSessions – one per server. The LLM could then have a suite of functions/tools corresponding to all those servers. Because MCP presents a consistent interface, the host manages each connection similarly, perhaps even in parallel if the AI needs to call multiple tools for one query. This scales well: whether the AI needs 2 tools or 20, the pattern remains the same. It’s up to the host’s logic (or the AI’s prompting) to decide which server to invoke for a given user request.

Alternate implementations: While the above pseudo-code is Python, MCP is language-agnostic. There are official SDKs in TypeScript, Java, Kotlin, C# etc., so one could implement the host side in, say, a Node.js app or a Java desktop program. Similarly, servers can be written in any language – as long as they adhere to the JSON-RPC message schema and protocol. This flexibility means MCP can be used in diverse environments: a web browser extension could run a JavaScript MCP server to expose the DOM to an AI agent, or an IoT hub could run a Rust-based MCP server to let an AI control smart home devices. The MCP spec ensures that all these will speak the same “language” to the AI client.

Conclusion

The Model Context Protocol is an emerging pillar in AI system architecture that aims to bridge the gap between isolated AI models and the rich world of data and tools they may need. Technically, MCP provides a unified, session-oriented RPC layer for context injection and tool use, drawing inspiration from prior standards (LSP for development tools, ODBC for databases, OpenAPI for service description, etc.). In practical terms, it enables AI developers to plug in capabilities at runtime rather than hardcoding them, and to do so in a model-agnostic way.

By comparing MCP with traditional APIs and middleware, we see that MCP is not about replacing existing APIs – it’s about abstracting and standardizing them for AI’s consumption. It deals with the nuances of AI integration (like maintaining conversational context, formatting results for prompts, handling tool invocation semantics) that general-purpose APIs or middleware don’t cover. As the ecosystem matures, we may see more services offering MCP endpoints out-of-the-box, and more AI platforms natively supporting MCP, thereby greatly simplifying the creation of context-aware, tool-using AI systems. Early adoption by companies in coding, productivity, and enterprise domains hints that MCP is solving real pain points. While it introduces an extra layer, its design is grounded in proven patterns (JSON RPC, client-server plugins) and seeks to deliver long-term efficiency by reducing duplicated integration work.

In essence, MCP provides the architecture for a future where AI assistants can easily “plug into” any system securely and reliably. Instead of a patchwork of bespoke integrations, developers can rely on the Model Context Protocol as a common backbone for AI connectivity. This clarity of separation (AI model on one side, MCP connectors on the other) will likely make AI/ML deployments more modular, maintainable, and powerful in the years to come. The protocol is still evolving (open-source contributions and discussions are ongoing), but it represents a significant step toward standardized AI middleware that could accelerate how we build complex AI-driven applications.

Sources: The description and analysis above draw on the official MCP documentation and specification, commentary from early adopters and analysts, as well as example implementations from community guides. All specific quotations and technical details are cited in the text.