Model Context Protocol: Building Secure Data Connections for AI Applications

Fred Rohrer

• 4 min read

What is the Model Context Protocol (MCP)?

The Model Context Protocol (MCP) is an open standard designed to create secure, bidirectional connections between data sources and AI applications. Instead of building custom integrations for each data source, MCP provides a standardized way for AI systems (clients) to access and interact with various information repositories (servers), maintaining context across different tools and datasets. This creates a more cohesive and efficient experience compared to traditional, fragmented integration approaches.

The architecture follows a client-server model:

  • MCP Servers: Expose data from various sources (e.g., Google Drive, Slack, databases, local files) and provide functionality through Tools, Prompts, and Resources.
  • MCP Clients: AI applications (like the Claude Desktop App, IDE extensions, or agent frameworks) that connect to MCP servers to access data and functionality. Clients manage the connection and may implement user interfaces for interacting with the server's capabilities.
  • Hosts: are LLM applications (like Claude Desktop or IDEs) that initiate connections.

Core Concepts and How They Work

MCP (Model Context Protocol) revolves around four key concepts:

  1. Resources:

    • Represent data exposed by servers to clients (e.g., files, database records, API responses, system data).
    • Identified by unique URIs (e.g., file:///path/to/file.txt, postgres://database/table).
    • Clients discover resources (using resources/list and resources/read requests) and subscribe to changes.
    • Crucially, resources are application-controlled: The client decides how and when to use them.

    Example: A server exposes log files as resources. A client allows the user to select a log file, fetches its contents via MCP, and provides it as context to an AI model.

  2. Prompts:

    • Predefined, reusable prompt templates offered by servers.
    • Accept arguments and include context from resources.
    • Enable standardized and shareable LLM interactions.
    • User-controlled: Users typically select prompts explicitly (e.g., as a slash command).

    Example: A server offers a "summarize-document" prompt. A client presents this as a slash command; the user types /summarize-document and selects a document (a resource) to summarize.

  3. Tools:

    • Allow servers to expose executable functionality to clients.
    • Enable LLMs to interact with external systems, perform computations, or take actions.
    • Model-controlled: The AI model can automatically invoke tools (often with human approval).
    • Defined with a name, description, and a JSON Schema for input parameters.

    Example: A server provides a "search-web" tool. The client, driven by the AI model, invokes the tool with a search query. The server executes the search and returns the results.

  4. Sampling:

    • Enables servers to request LLM completions through the client.
    • Essential for building agentic behaviors while maintaining security and privacy (client/user can review and modify requests/completions).
    • The server sends a sampling/createMessage request, specifying messages, model preferences, and context.
    • The client samples from an LLM and returns the result.

    Example: A server uses sampling to generate code based on a user's description and the contents of relevant files (resources). The user reviews the generated code.

Communication Flow: MCP uses JSON-RPC 2.0 over various transports (e.g., stdio for local communication, HTTP with Server-Sent Events (SSE) for remote connections). The protocol defines requests, responses, and notifications for interacting with resources, prompts, tools, and sampling.

Key Components Released

Three major components are available for developers:

  • MCP Specification and SDKs: The core protocol definition and SDKs (Python, TypeScript, Java, and Kotlin) to simplify building clients and servers.
  • Local MCP Server Support: Built into Claude Desktop applications, allowing connection to local MCP servers.
  • Open-Source Repository: Pre-built MCP servers for common systems (Google Drive, Slack, GitHub, Git, Postgres, Puppeteer).

Implementation with Claude 3.5 Sonnet

Claude 3.5 Sonnet has been optimized for building MCP server implementations, reducing the effort to connect datasets to AI tools. This simplifies data integration for organizations.

Early Adoption

Several companies are already leveraging MCP:

  • Block and Apollo: Integrated MCP into their systems.
  • Development Tools: Zed, Replit, Codeium, Sourcegraph, Cursor, Continue, GenAIScript, Goose, TheiaAI/TheiaIDE, Windsurf Editor, OpenSumi.
  • Other Clients: 5ire, BeeAI Framework, Cline, Emacs Mcp, Firebase Genkit, LibreChat, mcp-agent, oterm, Roo Code, SpinAI, Superinterface, Daydreams.

As Dhanji R. Prasanna, CTO at Block, noted: "Open technologies like the Model Context Protocol are the bridges that connect AI to real-world applications, ensuring innovation is accessible, transparent, and rooted in collaboration."

Benefits for Developers

MCP eliminates the need to maintain separate connectors for each data source. Developers can:

  • Build against a standard protocol: Implement MCP once, instead of building numerous one-off integrations.
  • Leverage existing MCP servers: Use pre-built servers for common systems, saving time.
  • Create a more sustainable integration architecture: Benefit from an expanding ecosystem.
  • Interoperability: Clients and servers built using MCP are interchangable.

Getting Started with MCP

Developers can start building and testing immediately:

  • Install pre-built servers through the Claude Desktop app.
  • Follow the quickstart guide to build an MCP server using the SDKs.
  • Test locally with Claude for Work to connect to internal systems.

All Claude.ai plans support connecting MCP servers to the Claude Desktop app. Claude for Work customers can test MCP servers locally. Developer toolkits for deploying remote production MCP servers are coming soon.

Technical Implications and Roadmap

MCP represents a significant shift towards standardized AI integration. The project is actively evolving, with priorities including:

  • Remote MCP Support: Adding authentication, authorization (especially OAuth 2.0), service discovery, and support for stateless operations.
  • Reference client example: to aid understanding of all MCP capabilities.
  • Distribution & Discovery: Exploring package management, server registries, and sandboxing for easier deployment and discovery.
  • Agent Support: Enhancing support for complex agentic workflows (hierarchical agents, interactive workflows, streaming results).
  • Broader Ecosystem: Expanding to support additional modalities (audio, video) and fostering community-led standards development.

Standardization through MCP will likely become increasingly important for building coherent, multi-step AI workflows across different data sources and applications. The community is encouraged to get involved through GitHub Discussions.

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