Claude’s Model Context Protocol (MCP): A Developer’s Guide

Anthropic’s Model Context Protocol (MCP) is an open source protocol that enables secure two-way communication between AI assistants and data sources such as databases, APIs and business tools. By using a client-server architecture, MCP standardizes the way AI models interact with external data, eliminating the need for custom integrations for each new data source.

Main components of MCP:

• Hosts: AI applications that initiate connections (e.g. Claude Desktop).
• Customers: Systems that maintain one-to-one connections to servers within the host application.
• Servers: Systems that provide context, tools, and prompts to customers.

Why MCP is important?

Simplifies integrations

Traditionally, connecting AI models to different data sources required custom code and solutions. MCP replaces this fragmented approach with a single, standardized protocol. This simplification speeds up development and reduces maintenance burden.

Improves AI capabilities

By providing AI models with seamless access to diverse data sources, MCP increases their ability to produce more relevant and accurate responses. This is particularly beneficial for tasks that require real-time data or specialized information.

Promotes safety

MCP is designed with safety in mind. Servers manage their own resources, eliminating the need to share sensitive API keys with AI providers. The protocol establishes clear system boundaries, making data access both controlled and auditable.

Cooperation

As an open source initiative, MCP encourages contributions from the developer community. This collaborative environment accelerates innovation and increases the range of available connectors and tools.

How MCP works

Architecture

MCP architecture

At its core, MCP follows a client-server architecture where a host application can connect to multiple servers. This setup allows AI applications to communicate seamlessly with different data sources.

Components:

• MCP hosts: Programs such as Claude Desktop, IDEs or AI tools that want to access resources via MCP.
• MCP customers: Protocol clients that maintain one-to-one connections to servers.
• MCP servers: Lightweight programs that each expose specific capabilities via the standardized Model Context Protocol.
• Local resources: The resources of your computer (databases, files, services) that MCP servers can securely access.
• External sources: Resources available over the Internet (for example, via APIs) that MCP servers can connect to.

Getting started with MCP

Requirements

• Claude desktop app: Available for macOS and Windows.
• SDKs: MCP offers SDKs for Typescript And Python.

Steps to get started

1. Install pre-built MCP servers: Start by setting up servers for commonly used data sources such as Google Drive, Slack, or GitHub via the Claude Desktop App.
2. Configure the host application: Edit the configuration file to include the MCP servers you want to use.
3. Build custom MCP servers: Use the included SDKs to create servers tailored to your specific data sources or tools.
4. Connect and test: Establish a connection between your AI application and the MCP server and start experimenting.

What’s happening under the hood?

When you interact with an AI application like Claude Desktop using MCP, several processes take place to facilitate communication and data exchange.

1. Server discovery

• Initialization: At startup, the MCP host (for example, Claude Desktop) connects to your configured MCP servers. This establishes the initial communication channels necessary for further interactions.

2. Protocol handshake

• Asset negotiation: The host application and MCP servers perform a handshake to negotiate capabilities and reach a common understanding.
• Identification: The host identifies which MCP server can handle a specific request based on the resources or functionality it makes available.

3. Interaction flow

Let’s look at an example where you query a local SQLite database via Claude Desktop.

MCP protocol

Step by step process:

1. Initialize the connection: Claude Desktop connects to the MCP server configured to interact with SQLite.
2. Available options: The MCP server communicates its capabilities, such as executing SQL queries.
3. Question request: You ask Claude Desktop to retrieve data. The host sends a query request to the MCP server.
4. SQL query execution: The MCP server executes the SQL query against the SQLite database.
5. Get results: The MCP server retrieves the results and sends them back to Claude Desktop.
6. Formatted results: Claude Desktop presents the data in a readable format.

More use cases

• Software development: Enhance code generation tools by connecting AI models to code repositories or issue trackers.
• Data analysis: Allow AI assistants to access and analyze datasets from databases or cloud storage.
• Business automation: Integrate AI with business tools such as CRM systems or project management platforms.

Benefits of the MCP architecture

• Modularity: By separating the host and servers, MCP enables modular development and easier maintenance.
• Scalability: Multiple MCP servers can be connected to a single host, with each server handling different resources.
• Interoperability: Standardizing communications through MCP allows different AI tools and sources to work together seamlessly.

Early Adopters and Community Support

Companies like it Repetition And Codeium are already adding support for MCP, and organizations love it Block And Apollo have implemented it. This growing ecosystem indicates strong industry support and a promising future for MCP.

Resources and further reading

Conclusion

The Model Context Protocol is a step forward in simplifying the way AI models interact with data sources. By standardizing these connections, MCP not only accelerates development, but also improves the capabilities of AI assistants. Anathopic does a great job of providing developers with the tools to use AI effectively.