Multi-Language MCP Server Performance Benchmark

Abstract

Table of Contents

1. Introduction & Motivation

The Model Context Protocol (MCP), introduced by Anthropic, has rapidly emerged as a foundational standard for connecting Large Language Models to enterprise data ecosystems. As organizations adopt MCP for production workloads, the choice of server implementation language becomes critical for scalability, resource efficiency, and operational costs.

While the MCP specification enables implementations across diverse programming languages, performance characteristics can vary dramatically based on language runtime behavior, concurrency models, memory management, and I/O handling strategies. This experiment aims to provide empirical data to inform architectural decisions for production MCP deployments.

2. Experimental Setup

2.1 Test Environment

All tests were conducted on a controlled environment to ensure reproducibility and eliminate external variables. Each server was containerized using Docker with identical resource constraints.

2.2 Server Implementations

Four MCP server implementations were developed, each following the Streamable HTTP transport specification. All servers expose identical functionality through four standardized benchmark tools, enabling fair performance comparison.

Server Ports: Java (8080), Go (8081), Python (8082), Node.js (8083)

2.3 Benchmark Tools & Configuration

Each server implements four identical tools designed to stress different performance dimensions:

Load testing was performed using k6, an open-source load testing tool. The benchmark script simulates realistic MCP client behavior with full session lifecycle:

// k6 Load Profile
export const options = {
    stages: [
        { duration: '10s', target: 50 },  // Ramp-up to 50 VUs
        { duration: '5m', target: 50 },   // Sustained load
        { duration: '10s', target: 0 },   // Ramp-down
    ],
    thresholds: {
        'http_req_failed': ['rate<0.05'],  // Error rate < 5%
    },
};

2.4 Test Methodology

The experimental methodology consisted of three independent test rounds to ensure statistical validity and identify consistency patterns. Each round followed this procedure:

graph LR
    A[Start Server] --> B[Health Check]
    B --> C[Start Stats Collection]
    C --> D[Execute k6 Load Test]
    D --> E[Stop Stats Collection]
    E --> F[Shutdown Server]
    F --> G[Aggregate Results]

    style A fill:#1e293b,stroke:#6366f1
    style G fill:#1e293b,stroke:#10b981
                    

1. Server Isolation: Only one server was tested at a time to eliminate resource contention.
2. Warmup Period: 10-second ramp-up allowed JIT compilation and connection pool initialization.
3. Sustained Load: 5-minute test period with 50 concurrent virtual users (VUs).
4. Metrics Collection: Parallel collection of HTTP metrics (k6) and resource metrics (Docker stats).
5. Cool-down: 10-second ramp-down for graceful shutdown.

3. Implementation Details

3.1 Java Server (Spring Boot + Spring AI)

The Java implementation leverages Spring Boot 4.0.0 and Spring AI 2.0.0-M2, utilizing declarative @McpTool annotations for tool registration. Running on Java 21, it benefits from recent performance improvements.

@McpTool(description = "Calculate Fibonacci number recursively")
public Map<String, Object> calculateFibonacci(
    @McpToolParameter(description = "Position in Fibonacci sequence") int n
) {
    return Map.of(
        "result", fibonacci(n),
        "server", "java"
    );
}

Key Characteristics:

• Compile-time type safety with annotation processing
• Synchronous I/O model
• JVM warmup benefits from JIT compilation
• Spring Boot Actuator for health monitoring

3.2 Go Server (Official SDK)

The Go implementation uses the official MCP SDK v1.2.0 compiled with Go 1.23. It exploits Go's goroutine-based concurrency model for efficient handling of concurrent requests.

server.AddTool("calculate_fibonacci", mcp.Tool{
    Description: "Calculate Fibonacci number recursively",
    InputSchema: map[string]interface{}{
        "type": "object",
        "properties": map[string]interface{}{
            "n": map[string]interface{}{
                "type": "integer",
                "description": "Position in Fibonacci sequence",
            },
        },
        "required": []string{"n"},
    },
}, func(args map[string]interface{}) (*mcp.ToolResponse, error) {
    n := int(args["n"].(float64))
    return &mcp.ToolResponse{
        Content: []interface{}{
            map[string]interface{}{
                "type": "text",
                "text": fmt.Sprintf(`{"result": %d, "server": "go"}`, fibonacci(n)),
            },
        },
    }, nil
})

Key Characteristics:

• Goroutines enable lightweight concurrency (thousands of concurrent requests)
• Static compilation eliminates runtime dependencies
• Minimal memory footprint (~18MB average)
• Efficient garbage collection with low pause times

3.3 Node.js Server (Official SDK)

The Node.js implementation uses @modelcontextprotocol/sdk v1.26.0 running on Node.js 20. It employs per-request MCP server instantiation as a security mitigation for CVE-2026-25536.

server.setRequestHandler(CallToolRequestSchema, async (request) => {
    if (request.params.name === 'calculate_fibonacci') {
        const n = request.params.arguments.n;
        const result = fibonacci(n);
        return {
            content: [{
                type: 'text',
                text: JSON.stringify({ result, server: 'nodejs' })
            }]
        };
    }
});

Key Characteristics:

• Event-driven I/O with libuv event loop
• Per-request server instantiation (security vs. performance trade-off)
• Single-threaded execution model
• Express.js for HTTP request handling

3.4 Python Server (FastMCP)

The Python implementation leverages FastMCP 2.12.0+ with FastAPI 0.109.0+ on Python 3.11, utilizing async/await for asynchronous I/O operations.

@mcp.tool()
async def calculate_fibonacci(n: int) -> dict:
    """Calculate Fibonacci number recursively."""
    result = fibonacci(n)
    return {"result": result, "server": "python"}

Key Characteristics:

• Decorator-based tool registration (@mcp.tool())
• AsyncIO event loop for concurrent request handling
• Global Interpreter Lock (GIL) limits CPU-bound parallelism
• FastAPI provides automatic OpenAPI documentation

4. Results & Analysis

4.1 Overall Performance Metrics

Across three test rounds totaling 3.9 million requests, clear performance tiers emerged. Java and Go demonstrated exceptional performance, Node.js showed moderate capability, while Python lagged significantly.

4.2 Latency Analysis

Average latency measurements reveal a dramatic performance gap between statically-compiled concurrent runtimes (Java with JVM JIT compilation and thread pooling, Go with goroutines) and single-threaded event-loop runtimes (Node.js V8 JIT engine, Python with GIL constraints).

Java and Go demonstrate remarkable consistency (standard deviation < 0.02ms), while Python shows higher variability with Round 2 exhibiting 12% higher latency than other rounds.

4.3 Throughput Comparison

Throughput measurements (requests per second) directly correlate with latency characteristics. Java and Go achieved virtually identical throughput, while Python managed only 18% of the high-performance tier.

4.4 Resource Efficiency

Resource efficiency metrics reveal critical trade-offs for production deployment decisions, particularly in cloud and containerized environments where resource costs directly impact operational expenses.

4.5 Tool-Specific Performance

Performance characteristics varied significantly across the four benchmark tools, revealing language-specific strengths and weaknesses in CPU-bound, I/O-bound, and data transformation workloads.

4.6 Percentile Analysis

Percentile latencies provide insight into tail latency characteristics crucial for user experience and SLA compliance.

Note: Values marked with "~" represent rounded approximations from k6's reported percentile distributions. All percentile values (p50, p90, p95, p99) are directly extracted from k6 metrics output without interpolation.

Java and Go demonstrate exceptional p50/p90 performance with sub-millisecond medians. The jump to p95 (~10ms) for both likely reflects the simulated database query tool with 10ms delay. Python's maximum latency of 233ms (Round 2) suggests occasional severe GIL contention or garbage collection pauses.

5. Discussion

5.1 Performance Tiers

The experimental results clearly segment the four implementations into three distinct performance tiers:

5.2 Trade-offs Analysis

Each implementation presents distinct trade-offs between performance, resource usage, developer experience, and ecosystem maturity.

* Cold start times were not measured empirically in this experiment. Values represent typical runtime characteristics based on general platform knowledge: JVM requires warmup and class loading, Go produces static binaries with minimal startup overhead, while Node.js and Python have moderate interpreter initialization times.

5.3 Consistency & Reliability

Performance consistency across test rounds provides insight into predictability and operational stability.

Python's Round 2 anomaly (88,290 requests vs. 95,910 in Round 1 and 96,405 in Round 3) suggests potential sensitivity to system state, garbage collection pressure, or GIL contention patterns that vary across runs.

6. Recommendations

6.1 Production Deployment Guidance

Based on experimental findings, the following decision framework provides guidance for selecting MCP server implementations for production deployments:

6.2 Use Case Decision Matrix

graph TD
    A[MCP Server Selection] --> B{Traffic Level?}
    B -->|High >1000 RPS| C{Memory Constrained?}
    B -->|Medium 500-1000| D[Go or Java]
    B -->|Low <500| E[Any Implementation]

    C -->|Yes| F[Go - Best Choice]
    C -->|No| G{Lowest Latency Priority?}
    G -->|Yes| H[Java]
    G -->|No| F

    D --> I{Cloud/K8s?}
    I -->|Yes| F
    I -->|No| J[Java or Go]

    E --> K{Team Expertise?}
    K -->|JS/TS| L[Node.js]
    K -->|Python| M[Python]
    K -->|Other| N[Go - Best Balance]

    style F fill:#10b981,stroke:#059669,color:#000
    style H fill:#10b981,stroke:#059669,color:#000
    style A fill:#1e293b,stroke:#6366f1
                    

7. Conclusion

This experimental analysis of MCP server implementations across four major programming languages provides empirical evidence for architectural decision-making in production deployments. Testing 3.9 million requests across three independent rounds revealed clear performance tiers and distinct trade-off profiles.

Key Finding: Go emerges as the optimal choice for production MCP deployments, delivering performance equivalent to Java (0.855ms vs 0.835ms average latency) while consuming 92% less memory (18MB vs 220MB). This combination of high performance and minimal resource footprint makes Go particularly well-suited for cloud-native, containerized, and cost-sensitive deployments.

Summary of Findings:

• Performance Tiers: Java and Go form a high-performance tier with sub-millisecond latencies and 1,600+ RPS throughput. Node.js and Python trail by 10-30x, suitable only for moderate to low-load scenarios.
• Resource Efficiency: Go demonstrates exceptional memory efficiency (92.6 RPS/MB) while Java excels in CPU efficiency (57.2 RPS/CPU%). Node.js and Python both exhibit CPU saturation (>93% utilization) with limited scaling headroom.
• Reliability: All implementations achieved 0% error rates across 3.9 million requests, demonstrating robust MCP protocol compliance. Go showed the highest consistency (0.5% variability), while Python exhibited 9.0% variability with occasional performance degradation.
• Tool-Specific Performance: Java excelled in CPU-bound tasks (Fibonacci: 0.369ms), while Go showed advantages in I/O operations (fetch: 1.292ms). Python's GIL significantly hampered CPU-bound performance (84x slower than Java for Fibonacci).

Future Work: This study focused on single-core performance with controlled resource limits. Future research will expand the scope to include:

• Alternative Java Runtimes: Benchmarking minimalist frameworks (e.g., Quarkus, Micronaut) and GraalVM Native Image to assess "bare metal" Java performance.
• Optimized Python & Node.js: Testing multi-worker Python configurations (with uvloop) and shared-instance Node.js architectures to measure maximum potential throughput.
• Real-world Scenarios: Examining multi-core scaling characteristics, mixed workload patterns, and behavior under resource contention.
• Advanced Features: Investigating streaming responses and bidirectional communication across implementations.

These extended benchmarks will provide a more granular view of the performance landscape, particularly for edge cases and highly optimized deployments.

The complete benchmark suite, including all test scripts, server implementations, and raw data, is available in the project repository for reproducibility and extended analysis.

8. References & Resources

1. Anthropic. (2024). Model Context Protocol Specification. https://modelcontextprotocol.io
2. Spring AI Team. (2025). Spring AI MCP Server Documentation. https://docs.spring.io/spring-ai/reference/
3. Anthropic. (2024). Go MCP SDK Repository. https://github.com/modelcontextprotocol/go-sdk
4. FastMCP Contributors. (2024). FastMCP Documentation. https://github.com/jlowin/fastmcp
5. Anthropic. (2024). Node.js MCP SDK Repository. https://github.com/modelcontextprotocol/typescript-sdk
6. Grafana Labs. (2024). k6 Load Testing Documentation. https://k6.io/docs/

9. Appendix

9.1 Raw Data and Complete Results

All raw benchmark data, including detailed results from all three test rounds, per-tool latency breakdowns, Docker stats logs, and k6 output files are available in the project repository:

The benchmark/results/ directory contains timestamped result sets for all test rounds, including:

9.2 Server Implementations

Complete source code for all four MCP server implementations is available in the repository:

• java-server/ - Spring Boot + Spring AI implementation
• go-server/ - Go SDK implementation
• python-server/ - FastMCP + FastAPI implementation
• nodejs-server/ - Node.js SDK implementation

9.3 Benchmark Suite

The complete benchmark suite and orchestration scripts are located in the benchmark/ directory:

• benchmark.js - k6 load testing script
• run_benchmark.sh - Automated benchmark orchestration
• collect_stats.py - Docker stats collection tool
• consolidate.py - Results aggregation script