The Model Context Protocol

Anthropic's Model Context Protocol (MCP) is an open-source standard for connecting AI assistants to various data sources. MCP employs a client-server architecture, enabling two-way communication between AI applications (clients) and data providers (servers) via different transports like stdio and HTTP with SSE. The protocol facilitates access to resources, tools, and prompts, enhancing AI response relevance and accuracy. Initial implementations include SDKs in Python and TypeScript, along with pre-built servers for common systems like Google Drive and GitHub. Discussion around MCP focuses on its potential to solve the problem of fragmented AI integrations and its role in the evolution of AI agents, while also addressing concerns regarding security and broad adoption.

MCP as a Universal Connector

The Model Context Protocol (MCP) facilitates communication between Large Language Models (LLMs) and data sources by establishing a standardised method for connecting them. This open-source protocol, developed by Anthropic, aims to solve the problem of fragmented data access that currently plagues AI systems.

Prior to MCP, integrating LLMs with different data sources required custom implementations for each source. This made it difficult to scale AI systems and limited their ability to access and utilise the vast amounts of data that exist in various formats and locations.

MCP solves this problem by providing a universal, open standard that allows developers to build secure, two-way connections between their data sources and AI-powered tools. Instead of creating individual connectors for each data source, developers can now use MCP to create one connector that can be used with any data source that also supports MCP. This significantly simplifies the process of connecting LLMs to data sources and makes it easier to scale AI systems.

MCP Architecture

MCP follows a client-server architecture, where hosts (LLM applications like Claude Desktop or IDEs) initiate connections. Clients operating within the host applications, maintain one-to-one connections with servers that provide context, tools, and prompts to the clients. The Model Context Protocol (MCP) architecture relies on three core components to facilitate communication between LLMs and external data sources:

MCP Servers: These servers function as **data gateways**, making resources, tools, and prompts accessible to AI applications. They expose data from sources like file systems, relational databases, code repositories, and various other systems to LLMs and agents. This is analogous to the role of servers in traditional client-server models, where the server hosts and provides access to resources.
MCP Clients: The AI-powered tools, such as Claude Desktop or IDEs, act as the **clients in this architecture**. They interact with the MCP servers to access the data and functionalities provided. They maintain one-to-one connections with the servers. Think of these as the applications that use the data provided by the servers to perform tasks or answer user queries.
Communication Layer: This layer is responsible for ensuring secure, bidirectional data exchange between local and remote resources. It acts as the bridge between clients and servers, enabling them to communicate effectively and securely. The protocol layer handles the specifics of this communication, managing message framing, request/response linking, and high-level communication patterns. Additionally, the transport layer handles the actual data transfer between clients and servers, supporting multiple transport mechanisms like standard input/output and HTTP with SSE.

These three components work together to enable AI applications to interact with external systems, retrieve necessary context, and ultimately produce better, more relevant responses.

Transport Mechanisms in the Model Context Protocol

The Model Context Protocol (MCP) supports multiple transport mechanisms which are responsible for the actual communication between clients and servers. These mechanisms determine how data is transferred over the network.

Stdio Transport: This mechanism utilises standard input/output for communication. It is considered ideal for local processes running on the same machine, as it offers high efficiency and simple process management.
HTTP with SSE Transport: This mechanism leverages Server-Sent Events (SSE) for server-to-client messages and HTTP POST for client-to-server messages. It is suitable for scenarios where HTTP compatibility is necessary, such as communicating with remote servers. However, security implications, including authentication and authorisation, should be considered when using this transport for remote communication.

All transport mechanisms within MCP utilise JSON-RPC 2.0 for message exchange. JSON-RPC is a lightweight remote procedure call protocol that uses JSON for data structuring and provides a standard format for requests, responses, notifications, and errors. This ensures consistency and interoperability across different implementations of MCP.

The choice of transport mechanism depends on the specific use case and the environment in which the client and server are operating. For communication between processes on the same machine, the stdio transport offers simplicity and efficiency. When connecting to remote servers or requiring HTTP compatibility, the HTTP with SSE transport is a suitable choice.

Primary Message Types in MCP

The Model Context Protocol (MCP) uses several primary message types for communication between clients and servers:

Requests are messages sent from one party (client or server) to the other, expecting a response. Requests must include a method field indicating the requested action and may optionally include a params field containing any parameters required for the request.
Notifications are one-way messages that do not expect a response. Similar to requests, notifications have a method field indicating the action being communicated and an optional params field for any additional information.
Results are responses to requests, indicating successful completion of the requested action. Results can contain arbitrary data, including key-value pairs, depending on the specific request.
Errors are messages indicating that a request failed. Error messages contain an error code, a human-readable message explaining the error, and an optional data field for additional error-related information. MCP defines standard error codes, but SDKs and applications can define their own codes as well.

These four message types form the basis of all communication within the MCP protocol. They allow clients and servers to exchange information, request actions, report progress, and handle errors in a structured and reliable manner.

Benefits of MCP

MCP offers several benefits:

Simplified Integration: Developers no longer need to build custom integrations for each data source.
Scalability: MCP makes it easier to connect LLMs to a large number of data sources, improving scalability.
Improved Contextual Awareness: LLMs can access a wider range of data, leading to more accurate and relevant responses.
Increased Functionality: MCP enables AI agents to perform more complex tasks by interacting directly with external systems.

Examples of MCP in Action

Several companies have already adopted MCP, demonstrating its potential:

Block and Apollo have integrated MCP into their systems.
Development tools companies like Zed, Replit, Codeium, and Sourcegraph are using MCP to enhance their platforms.
Several pre-built MCP servers exist for popular enterprise systems like Google Drive, Slack, GitHub, Git, Postgres, and Puppeteer.

Future of MCP

The future of MCP hinges on industry-wide adoption and collaborative standardisation efforts. The participation of major AI players is crucial in driving this adoption and ensuring interoperability between different AI systems. If successful, MCP has the potential to become a foundational technology for AI integration, similar to the role SOA protocols played in web services.

Photo by Google DeepMind