Anthropic's Model Context Protocol: A Blueprint for AI to SaaS Interconnectivity

When I first encountered Anthropic's Model Context Protocol (MCP), I saw more than just another specification—I saw a bridge between worlds. The AI world is rapidly evolving with models like Claude, and the vast ecosystem of tools and services we've built over decades. What if connecting these worlds could be as simple as writing a few lines of code?

That question led me down a rabbit hole of exploration, experimentation, and ultimately, creation. Today, I'm excited to share the journey of building MCPR, a Rust implementation of the Model Context Protocol that's now available on crates.io.

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The Spark: Why MCP Matters

Before diving into the technical details, let's address the elephant in the room:

Why does MCP matter?

In the current AI landscape, we have incredibly powerful models that can reason, generate, and understand—but they're often isolated from the tools and services that make up our digital infrastructure. MCP changes that by providing a standardized way for AI assistants to communicate with external tools and data sources.

Imagine an AI that can not only discuss your codebase but actually run tests, query databases, or search documentation—all through a consistent, well-defined protocol. That's the promise of MCP.

The Journey Begins: Understanding the Protocol

My journey started with a deep dive into the MCP specification. As I pored over the documentation, I realized that MCP is elegantly simple at its core:

1. A client (typically an AI assistant) connects to a server

2. The server provides a list of available tools

3. The client calls these tools with specific parameters

4. The server executes the tools and returns results

This client-server architecture, built on JSON-RPC 2.0, provides a flexible foundation for AI-tool interactions. But understanding the specification was just the beginning—implementing it in Rust would be the real challenge.

Building Blocks: The Architecture of MCPR

As I began sketching out the architecture for MCPR, I wanted to create something that was both true to the specification and idiomatic to Rust. This meant embracing Rust's strong type system, error handling, and async capabilities.

The SDK needed several core components:

Schema definitions for all MCP message types

Transport layer for communication between clients and servers

High-level client and server implementations for easy integration

Project generator to scaffold new MCP applications

Each component presented its own challenges, but the transport layer was particularly interesting.

MCP supports multiple transport mechanisms, including stdio (for local processes) and Server-Sent Events (for web applications). Implementing these in Rust required careful consideration of async patterns, error handling, and resource management.

// A simplified example of the transport trait

pub trait Transport {    
  async fn send(&mut self, message: JSONRPCMessage) -> Result<()>;      
  async fn receive(&mut self) -> Result<JSONRPCMessage>;    async fn 
  close(&mut self) -> Result<()>;
}

The Breakthrough: Template Generation

While building the core SDK was satisfying, I wanted to make it truly accessible to developers. That's when I had the idea for a template generator—a tool that could scaffold complete MCP projects with just a few commands.

mcpr generate-project --name my-project --transport stdio

This simple command would generate a complete project structure with both client and server components, ready to be customized. The templates would include all the boilerplate code needed to establish connections, register tools, and handle messages.

Implementing this generator was a fascinating exercise in metaprogramming. I needed to create templates that were both flexible enough to accommodate different transport types and specific enough to be immediately useful.

The breakthrough came when I realized I could use a layered approach:

Core templates for the basic project structure

Transport-specific templates for connection handling

Tool templates for common functionality

This approach allowed me to generate projects that were ready to run out of the box, while still being customizable for specific use cases.

The Test Case: GitHub Tools Example

To validate the SDK and demonstrate its capabilities, I built a complete example application: GitHub Tools. This project showcases how MCP can be used to create AI-powered tools that interact with external services.

The GitHub Tools example includes:

1. A server that provides tools for querying GitHub repositories

2. A client that connects to the server and calls these tools

3. Support for multiple transport types

One of the most interesting aspects of this example is how it demonstrates the client-server disconnect. The server can run independently, and multiple clients can connect to it simultaneously. This architecture enables scenarios where:

The server runs as a background service
Multiple AI assistants can use the same tools
The system is resilient to client failures

Here's a snippet from the client code that shows how simple it is to call a tool:

// Connect to the server

let transport = StdioTransport::new();
let mut client = Client::new(transport);
client.initialize()?;

// Call the readme_query tool

let response = client.call_tool::<Value, Value>(
    "readme_query",
    &serde_json::json!({
        "repo": "rust-lang/rust",
        "query": "What is Rust used for?"
    })
)?;

println!("Answer: {}", response["answer"]);

Building this example was not just a validation exercise—it was a journey of discovery. I found edge cases I hadn't considered, improved error handling, and refined the API to be more intuitive.

The Revelation: MCP as a Bridge Between Worlds

As I worked on MCPR, I had a revelation: MCP isn't just a protocol for AI assistants to call tools—it's a bridge between worlds.

On one side, we have modern AI systems with their natural language understanding and generation capabilities. On the other, we have decades of software infrastructure—APIs, databases, command-line tools, and more.

MCP connects these worlds in a way that leverages the strengths of both. AI assistants can maintain their conversational interface while gaining the ability to interact with structured systems. Legacy services can be exposed to AI without requiring a complete rewrite.

This bridging capability is particularly valuable in enterprise settings, where organizations have invested heavily in existing infrastructure. With MCP, they can gradually introduce AI capabilities without disrupting their core systems.

The Future: Endless Possibilities

As I reflect on the journey of building MCPR, I'm excited about the possibilities it opens up. The SDK is now available on crates.io (version 0.2.2), and I'm eager to see what the community builds with it.

Some of the possibilities I'm particularly excited about:

1. IDE integrations that allow AI assistants to interact with your codebase

2. Data analysis tools that combine natural language queries with powerful data processing

3. DevOps assistants that can monitor systems and execute commands

4. Research tools that can search, synthesize, and summarize information

The GitHub Tools example is just the beginning. With MCPR, developers can create custom tools that leverage the unique capabilities of their systems and expose them to AI assistants through a standardized interface.

Throughout this journey, I've gained a deeper appreciation for the value of well-designed protocols. MCP's simplicity and flexibility make it a powerful foundation for AI-tool interactions.

Contributers welcome

I should note that MCPR is very much a passion project, born from weekend coding sessions fueled by curiosity and coffee. It's not production-ready software backed by a team of engineers—it's the result of one developer's fascination with a protocol that could reshape how we interact with AI. I built it because I was intrigued by the possibilities, because I wanted to understand MCP at a deeper level, and because I believe in the power of open-source exploration. While I've tested it extensively in my own environments, it lacks the battle-testing that comes with widespread production use. Consider it a starting point, a foundation to build upon, or simply an educational journey through the mechanics of MCP. The code is there for you to explore, extend, and perhaps even transform into something production-worthy if the spirit moves you.

If you're interested in exploring MCP and building AI-powered tools, I invite you to check out MCPR on GitHub and crates.io

The SDK includes:

• Complete implementation of the MCP schema

• Multiple transport options (stdio and SSE)

• High-level client and server implementations

• Project generator for quick scaffolding

• Comprehensive documentation and examples

Whether you're building a simple command-line tool or a complex enterprise application, MCPR provides the building blocks you need to connect your systems to the world of AI.

The journey from protocols to possibilities is just beginning, and I'm excited to see where it leads.