$ cat building-mcp-servers.md

Having architected distributed systems across enterprise environments for over a decade, the Model Context Protocol represents a paradigm shift that addresses fundamental challenges in AI tooling infrastructure. Through the development of production-grade MCP servers including gopher-mcp and openzim-mcp, I’ve identified architectural patterns and implementation strategies that demonstrate MCP’s potential to revolutionize how AI systems interact with external resources.

Update (June 2025): I’ve split this comprehensive guide into two focused articles for better readability:

• Gopher MCP Server: Bringing 1991’s Internet to Modern AI - Focuses on the Gopher protocol, its history, and practical applications
• OpenZIM MCP Server: Offline Knowledge for AI Assistants - Covers offline Wikipedia access and ZIM format optimization

Understanding the Model Context Protocol Architecture

The Model Context Protocol addresses a critical gap in AI system architecture: the secure, standardized integration of external resources without compromising system integrity or performance. This protocol establishes a formal contract between AI models and external data sources, eliminating the ad-hoc integration patterns that have plagued enterprise AI deployments.

MCP functions as an abstraction layer that enables AI models to interact with heterogeneous external resources—from legacy protocol implementations to modern API endpoints—through a unified interface. This architectural approach reflects decades of distributed systems engineering principles applied to the unique challenges of AI tooling.

Strategic Advantages of MCP Implementation

• Zero-Trust Security Model: Implements capability-based security with explicit permission boundaries, eliminating the attack vectors inherent in traditional plugin architectures
• Protocol Standardization: Establishes consistent interaction patterns that reduce integration complexity and maintenance overhead across diverse resource types
• Horizontal Scalability: Designed for extensibility without architectural debt, enabling rapid capability expansion without system redesign
• Performance Optimization: Native support for caching, connection pooling, and resource lifecycle management that scales with enterprise workloads

Architectural Patterns for Production MCP Systems

Through the implementation of multiple production-grade MCP servers, several critical architectural patterns have emerged that address scalability, maintainability, and operational concerns. These patterns reflect established principles from distributed systems engineering, adapted for the unique requirements of AI resource integration:

1. Resource-Centric Design

#[derive(Debug, Clone)]
pub struct Resource {
    pub uri: String,
    pub name: String,
    pub description: Option<String>,
    pub mime_type: Option<String>,
}

pub trait ResourceProvider {
    async fn list_resources(&self) -> Result<Vec<Resource>, Error>;
    async fn read_resource(&self, uri: &str) -> Result<Vec<u8>, Error>;
}

This abstraction implements the Strategy pattern at the infrastructure level, enabling runtime backend substitution without affecting core business logic. The separation of concerns between resource discovery and access provides the foundation for implementing sophisticated caching strategies, load balancing, and failover mechanisms essential for production deployments.

2. Protocol Abstraction Layer

The gopher-mcp implementation required supporting multiple protocol families (Gopher and Gemini), presenting an opportunity to demonstrate protocol abstraction at scale. Rather than implementing protocol-specific handlers in isolation, a unified abstraction layer enables consistent behavior across diverse protocol implementations:

pub trait ProtocolHandler {
    async fn fetch(&self, url: &str) -> Result<ProtocolResponse, Error>;
    fn supports_url(&self, url: &str) -> bool;
}

pub struct GopherHandler;
pub struct GeminiHandler;

impl ProtocolHandler for GopherHandler {
    async fn fetch(&self, url: &str) -> Result<ProtocolResponse, Error> {
        // Gopher-specific implementation
    }

    fn supports_url(&self, url: &str) -> bool {
        url.starts_with("gopher://")
    }
}

This architectural approach demonstrates the Open/Closed Principle in practice—the system remains open for extension while closed for modification. Protocol addition becomes a matter of trait implementation rather than core system modification, ensuring system stability while enabling rapid capability expansion.

3. Async-First Architecture

Production MCP servers must handle concurrent request loads while maintaining sub-millisecond response times for cached resources. Blocking I/O operations represent a fundamental scalability bottleneck that can cascade through the entire system. Rust’s async runtime provides the foundation for building truly concurrent systems without the complexity overhead of traditional threading models:

use tokio::sync::RwLock;
use std::collections::HashMap;

pub struct CachedResourceProvider {
    cache: RwLock<HashMap<String, CachedResource>>,
    provider: Box<dyn ResourceProvider + Send + Sync>,
}

impl CachedResourceProvider {
    pub async fn get_resource(&self, uri: &str) -> Result<Vec<u8>, Error> {
        // Check cache first
        {
            let cache = self.cache.read().await;
            if let Some(cached) = cache.get(uri) {
                if !cached.is_expired() {
                    return Ok(cached.data.clone());
                }
            }
        }

        // Fetch and cache
        let data = self.provider.read_resource(uri).await?;
        let mut cache = self.cache.write().await;
        cache.insert(uri.to_string(), CachedResource::new(data.clone()));

        Ok(data)
    }
}

Case Study: OpenZIM MCP Server Architecture

The openzim-mcp implementation addresses the complex challenge of providing sub-second search capabilities across compressed knowledge bases containing millions of articles. This represents a classic systems engineering problem: optimizing for both storage efficiency and query performance while maintaining memory constraints suitable for edge deployment scenarios.

ZIM File Handling

The fundamental challenge involves implementing efficient search algorithms over compressed data structures without incurring the computational overhead of full decompression. This requires sophisticated indexing strategies that balance memory utilization against query performance—a problem domain that intersects information retrieval, data compression theory, and systems optimization.

use zim::Zim;
use tantivy::{Index, schema::*, collector::TopDocs};

pub struct ZimResourceProvider {
    zim: Zim,
    search_index: Index,
}

impl ZimResourceProvider {
    pub async fn search(&self, query: &str, limit: usize) -> Result<Vec<SearchResult>, Error> {
        let reader = self.search_index.reader()?;
        let searcher = reader.searcher();

        let query_parser = QueryParser::for_index(&self.search_index, vec![self.content_field]);
        let query = query_parser.parse_query(query)?;

        let top_docs = searcher.search(&query, &TopDocs::with_limit(limit))?;

        let mut results = Vec::new();
        for (_score, doc_address) in top_docs {
            let retrieved_doc = searcher.doc(doc_address)?;
            results.push(self.doc_to_search_result(retrieved_doc)?);
        }

        Ok(results)
    }
}

Performance Tricks I Discovered

The optimization strategy implements several critical performance patterns:

1. Demand-Driven Resource Loading: Implements lazy evaluation patterns to minimize memory footprint and initialization overhead
2. Inverted Index Architecture: Leverages Tantivy’s Lucene-inspired indexing for O(log n) search complexity across massive document collections
3. Memory-Mapped I/O: Delegates page cache management to the kernel, enabling efficient memory utilization without explicit cache implementation
4. Resource Pool Management: Implements connection pooling patterns to amortize expensive resource initialization costs across request lifecycles

Case Study: Gopher MCP Server Implementation

The gopher-mcp server demonstrates how legacy protocol implementations can provide valuable insights into minimalist system design. The Gopher protocol’s simplicity—predating the complexity layers that characterize modern web protocols—offers architectural lessons about the relationship between protocol complexity and system reliability.

Protocol Implementation

use tokio::net::TcpStream;
use tokio::io::{AsyncReadExt, AsyncWriteExt};

pub struct GopherClient;

impl GopherClient {
    pub async fn fetch(&self, url: &GopherUrl) -> Result<GopherResponse, Error> {
        let mut stream = TcpStream::connect((url.host.as_str(), url.port)).await?;

        // Send Gopher request
        let request = format!("{}\r\n", url.selector);
        stream.write_all(request.as_bytes()).await?;

        // Read response
        let mut buffer = Vec::new();
        stream.read_to_end(&mut buffer).await?;

        Ok(GopherResponse::parse(buffer, url.item_type)?)
    }
}

Content Type Detection

Gopher uses a simple but effective type system:

#[derive(Debug, Clone, Copy)]
pub enum GopherItemType {
    TextFile = b'0',
    Directory = b'1',
    PhoneBook = b'2',
    Error = b'3',
    BinHexFile = b'4',
    BinaryFile = b'9',
    // ... more types
}

impl GopherItemType {
    pub fn to_mime_type(self) -> &'static str {
        match self {
            Self::TextFile => "text/plain",
            Self::Directory => "text/gopher-menu",
            Self::BinaryFile => "application/octet-stream",
            // ... more mappings
        }
    }
}

Best Practices for MCP Server Development

1. Error Handling

Implement comprehensive error handling with context:

use thiserror::Error;

#[derive(Error, Debug)]
pub enum McpError {
    #[error("Network error: {0}")]
    Network(#[from] std::io::Error),

    #[error("Protocol error: {message}")]
    Protocol { message: String },

    #[error("Resource not found: {uri}")]
    ResourceNotFound { uri: String },
}

2. Configuration Management

Use structured configuration with validation:

use serde::{Deserialize, Serialize};

#[derive(Debug, Deserialize, Serialize)]
pub struct ServerConfig {
    pub bind_address: String,
    pub max_connections: usize,
    pub cache_size: usize,
    pub timeout_seconds: u64,
}

impl Default for ServerConfig {
    fn default() -> Self {
        Self {
            bind_address: "127.0.0.1:8080".to_string(),
            max_connections: 100,
            cache_size: 1024 * 1024 * 100, // 100MB
            timeout_seconds: 30,
        }
    }
}

3. Testing Strategy

Implement comprehensive testing including integration tests:

#[cfg(test)]
mod tests {
    use super::*;
    use tokio_test;

    #[tokio::test]
    async fn test_resource_provider() {
        let provider = MockResourceProvider::new();
        let result = provider.read_resource("test://example").await;
        assert!(result.is_ok());
    }

    #[tokio::test]
    async fn test_protocol_handler() {
        let handler = GopherHandler::new();
        assert!(handler.supports_url("gopher://example.com/"));
        assert!(!handler.supports_url("http://example.com/"));
    }
}

Performance Considerations

Memory Management

• Use streaming for large resources
• Implement proper caching strategies
• Monitor memory usage in production

Concurrency

• Design for high concurrency from the start
• Use appropriate synchronization primitives
• Consider backpressure mechanisms

Network Efficiency

• Implement connection pooling
• Use compression when appropriate
• Handle network timeouts gracefully

Deployment and Monitoring

Docker Deployment

FROM rust:1.75 as builder
WORKDIR /app
COPY . .
RUN cargo build --release

FROM debian:bookworm-slim
RUN apt-get update && apt-get install -y ca-certificates
COPY --from=builder /app/target/release/mcp-server /usr/local/bin/
EXPOSE 8080
CMD ["mcp-server"]

Health Checks

Implement health check endpoints for monitoring:

pub async fn health_check() -> impl Reply {
    warp::reply::with_status("OK", StatusCode::OK)
}

Future Directions in MCP Architecture

The MCP ecosystem represents an emerging infrastructure layer with significant implications for enterprise AI deployment strategies. Several architectural evolution paths warrant investigation:

• Streaming Protocol Extensions: Implementing backpressure-aware streaming for large dataset processing without memory exhaustion
• Zero-Trust Authentication Models: Developing capability-based security frameworks that scale across federated MCP deployments
• Distributed MCP Federations: Architecting service mesh patterns for MCP server orchestration and load distribution
• Observability Infrastructure: Implementing distributed tracing and metrics collection for complex MCP interaction patterns

Strategic Implications and Future Outlook

The development of production-grade MCP servers reveals fundamental patterns that will shape the next generation of AI infrastructure. These implementations demonstrate that the Model Context Protocol represents more than a technical specification—it embodies a architectural philosophy that prioritizes security, scalability, and operational excellence.

The strategic insight emerging from this work centers on progressive complexity management: begin with minimal viable implementations, establish comprehensive observability, and iterate based on production feedback. The Model Context Protocol’s maturation trajectory suggests it will become foundational infrastructure for enterprise AI deployments, requiring the same engineering rigor applied to other critical system components.

The architectural patterns documented here provide a foundation for building AI systems that are not merely functional, but operationally excellent—systems that scale gracefully, fail safely, and evolve sustainably as requirements change.

Dive Deeper

For more focused, practical guides on building specific types of MCP servers, check out these detailed articles:

• Gopher MCP Server: Bringing 1991’s Internet to Modern AI - Learn about implementing protocol handlers, Gopher’s fascinating history, and practical applications for alternative internet protocols
• OpenZIM MCP Server: Offline Knowledge for AI Assistants - Discover how to build offline knowledge systems, optimize ZIM file handling, and create AI assistants that work without internet connectivity

Want to explore these concepts further? Check out the gopher-mcp and openzim-mcp repositories for complete implementations.