MCP (Model Context Protocol) Based Multi-Agent System Design and Implementation Guide

Recently, in the generative AI domain, Multi-Agent Systems are gaining attention to overcome the limitations of single LLMs (Large Language Models). In particular, with the emergence of MCP (Model Context Protocol) for standardized communication between agents, designing scalable and modular architectures has become essential.

This post explores how to design and implement efficient multi-agent architectures using MCP in high-performance agent runtime environments like ZeroClaw. We’ve included architectural design principles and actual code examples so readers can apply them immediately.

1. MCP-Based Multi-Agent Architecture Design

The most crucial aspect when building a multi-agent system is lowering coupling and increasing cohesion. To achieve this, MCP adopts a client-server model, where each agent or tool operates as an independent server and communicates through a standard protocol (based on JSON-RPC 2.0).

Key Design Principles

Standardized Interface: All agents must adhere to the MCP standard to expose tools, resources, and prompts.
Asynchronous Communication: For agents with long task execution times (e.g., file processing, web scraping), an asynchronous message queue should be utilized.
Statelessness: Agent servers should be designed to be as stateless as possible to facilitate horizontal scaling.

Architecture Diagram (Conceptual)

[User/Client LLM] <--> [MCP Gateway (Orchestrator)]
                           |         |         |
                           v         v         v
                      [Blog Server] [Discord MCP] [Cloud Monitor]
                      (Rust/TS)    (Gateway)      (Agent)

In this structure, the Gateway (Orchestrator) analyzes user requests, delegates tasks to the appropriate MCP servers (agents), and aggregates the results to return them to the user.

2. Implementing MCP Servers: Building Lightweight Agents with Rust

We will implement a simple MCP server in Rust to achieve high performance in environments like blog-api-server or ZeroClaw. This server provides a simple tool that returns the current time.

Dependency Setup (Cargo.toml)

We will use Rust’s powerful asynchronous runtime tokio, serde for JSON processing, and hyper or jsonrpc for MCP communication.

[dependencies]
tokio = { version = "1", features = ["full"] }
serde = { version = "1.0", features = ["derive"] }
serde_json = "1.0"
hyper = { version = "0.14", features = ["server", "http1"] }

MCP Server Code Example

The following is a simplified implementation of a standard MCP server that communicates via STDIN/STDOUT.

use serde::{Deserialize, Serialize};
use serde_json::{Value, json};
use std::io::{self, BufRead, Write};

// MCP Request/Response structure definitions
#[derive(Deserialize)]
struct MCPRequest {
    jsonrpc: String,
    id: Option<String>,
    method: String,
    params: Option<Value>,
}

#[derive(Serialize)]
struct MCPResponse {
    jsonrpc: String,
    id: Option<String>,
    result: Option<Value>,
    error: Option<Value>,
}

fn handle_request(req: MCPRequest) -> MCPResponse {
    match req.method.as_str() {
        "initialize" => {
            // Called when the client wants to check the server's capabilities
            MCPResponse {
                jsonrpc: "2.0".to_string(),
                id: req.id,
                result: Some(json!({
                    "protocolVersion": "2024-11-05",
                    "capabilities": {
                        "tools": {}
                    },
                    "serverInfo": {
                        "name": "my-rust-agent",
                        "version": "0.1.0"
                    }
                })),
                error: None,
            }
        }
        "tools/list" => {
            // Returns a list of available tools
            MCPResponse {
                jsonrpc: "2.0".to_string(),
                id: req.id,
                result: Some(json!({
                    "tools": [
                        {
                            "name": "get_current_time",
                            "description": "Returns the current server time",
                            "inputSchema": {
                                "type": "object",
                                "properties": {}
                            }
                        }
                    ]
                })),
                error: None,
            }
        }
        "tools/call" => {
            // Actual tool execution logic
            if let Some(params) = req.params {
                let tool_name = params["name"].as_str().unwrap_or("");
                if tool_name == "get_current_time" {
                    return MCPResponse {
                        jsonrpc: "2.0".to_string(),
                        id: req.id,
                        result: Some(json!({
                            "content": [
                                {
                                    "type": "text",
                                    "text": format!("Current time: {}", chrono::Utc::now().to_rfc3339())
                                }
                            ]
                        })),
                        error: None,
                    };
                }
            }
            // Error handling
            MCPResponse {
                jsonrpc: "2.0".to_string(),
                id: req.id,
                result: None,
                error: Some(json!({
                    "code": -32601,
                    "message": "Tool not found"
                })),
            }
        }
        _ => MCPResponse {
            jsonrpc: "2.0".to_string(),
            id: req.id,
            result: None,
            error: Some(json!({ "code": -32601, "message": "Method not found" })),
        },
    }
}

fn main() {
    let stdin = io::stdin();
    let mut stdout = io::stdout();

    for line in stdin.lock().lines() {
        let line = line.unwrap();
        if let Ok(req) = serde_json::from_str::<MCPRequest>(&line) {
            let res = handle_request(req);
            let res_json = serde_json::to_string(&res).unwrap() + "\n";
            stdout.write_all(res_json.as_bytes()).unwrap();
            stdout.flush().unwrap();
        }
    }
}

This code handles the core MCP methods initialize, tools/list, and tools/call, enabling LLMs (e.g., Claude Code) to recognize and invoke this agent as a tool.

3. Agent Communication and Data Synchronization

In architectures like ZeroClaw, where multiple agents collaborate, data sharing is crucial. A file-based architecture allows agents to exchange data while reducing coupling.

Consider a scenario where Agent A generates data, and Agent B consumes it.

Agent A (Producer): Saves the computation result to /tmp/shared/task_result.json.
Agent B (Consumer): Reads the resource file://tmp/shared/task_result.json via MCP, or calls a separate read_file tool.

// Example of Agent A calling a tool (pseudo-code)
fn save_result(data: &str) -> std::io::Result<()> {
    fs::write("/tmp/shared/task_result.json", data)?;
    Ok(())
}

This approach is useful when message queues are complex and effective for continuously tracking data, such as in the logging system or monitoring dashboard of blog-api-server.

4. Conclusion and Future Directions

Leveraging MCP allows for the modularization and maintainability of complex multi-agent systems.

Advantages: Language-agnostic interface (can mix Rust, TypeScript, etc.), tool reusability, standardized error handling.
Considerations: STDIN/STDOUT communication overhead, load balancing for the Gateway to handle large traffic.

The future goal is to build an environment within the ZeroClaw runtime where these MCP servers are containerized and dynamically scaled as needed. This will enable LLMs to evolve beyond simple chatbots into intelligent agent networks that automate complex tasks.