绿色计算研究MCP服务器基准测试指南

概述

本指南提供了在MCP（模型上下文协议）服务器上进行基准测试实验的详细说明，以便为您的节能MCP架构调查论文生成经验数据。

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1.环境设置

1.1硬件要求文件

在基准测试之前，记录您的系统规格以确保可重复性：

# Get system information
systeminfo | Select-String "OS Name|OS Version|System Type|Total Physical Memory|Processor"

# Get CPU details
Get-WmiObject -Class Win32_Processor | Select-Object Name, NumberOfCores, NumberOfLogicalProcessors, MaxClockSpeed

# Get RAM details
Get-WmiObject -Class Win32_PhysicalMemory | Measure-Object -Property Capacity -Sum

# Get disk type (SSD/HDD)
Get-PhysicalDisk | Select-Object MediaType, Size, FriendlyName

1.2软件先决条件

安装Node.js（用于JavaScript MCP服务器）

# Using winget
winget install OpenJS.NodeJS.LTS

# Verify installation
node --version
npm --version

安装Python（适用于Python MCP服务器）

# Using winget
winget install Python.Python.3.12

# Verify installation
python --version
pip --version

安装Docker（用于容器化基准测试）

# Download Docker Desktop from https://www.docker.com/products/docker-desktop/
# Or using winget
winget install Docker.DockerDesktop

# Verify installation
docker --version

安装UV（MCP的快速Python包管理器）

# Install UV
pip install uv

# Or using PowerShell
irm https://astral.sh/uv/install.ps1 | iex

1.3 MCP SDK安装

# Create benchmark workspace
mkdir C:\MCP_Benchmarks
cd C:\MCP_Benchmarks

# Install MCP SDK for Python
pip install mcp

# Install MCP SDK for TypeScript/JavaScript
npm init -y
npm install @modelcontextprotocol/sdk

1.4安装克劳德桌面（MCP主机）

下载地址：https://claude.ai/download

在以下位置配置MCP服务器： %APPDATA%\Claude\claude_desktop_config.json

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2.基准情景

根据您的调查，以下是要进行基准测试的关键场景：

场景1：代币消费比较

目标： 衡量传统ReAct与渐进式披露模式中的代币使用情况

场景2：传输机制延迟

目标： 比较stdio、HTTP和SSE传输延迟

场景3：序列化开销

目标： 衡量JSON-RPC序列化成本与替代方案

场景4：缓存效率

目标： 量化缓存策略带来的性能改进

场景5：资源利用

目标： 测量不同服务器类型的CPU、内存和能耗

场景6：多服务器可扩展性

目标： 随着服务器数量的增加，测试性能下降

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3.测量工具

3.1能源计量工具

Windows内置电源监控

# Enable energy estimation (requires admin)
powercfg /energy /duration 60

# View power usage
powercfg /batteryreport

英特尔电源小工具（适用于英特尔CPU）

下载：https://www.intel.com/content/www/us/en/developer/articles/tool/power-gadget.html

HWiNFO64（综合硬件监控）

下载：https://www.hwinfo.com/download/

打开硬件监视器（CPU/GPU电源）

# Install via Chocolatey
choco install openhardwaremonitor

3.2性能监控工具

自定义PowerShell监控脚本

# Save as: C:\MCP_Benchmarks\monitor.ps1

param(
    [int]$DurationSeconds = 60,
    [int]$IntervalMs = 1000,
    [string]$OutputFile = "metrics.csv"
)

$metrics = @()
$endTime = (Get-Date).AddSeconds($DurationSeconds)

while ((Get-Date) -lt $endTime) {
    $cpu = Get-Counter '\Processor(_Total)\% Processor Time' -ErrorAction SilentlyContinue
    $mem = Get-Counter '\Memory\Available MBytes' -ErrorAction SilentlyContinue
    $disk = Get-Counter '\PhysicalDisk(_Total)\Disk Bytes/sec' -ErrorAction SilentlyContinue
    
    $metrics += [PSCustomObject]@{
        Timestamp = Get-Date -Format "yyyy-MM-dd HH:mm:ss.fff"
        CPU_Percent = [math]::Round($cpu.CounterSamples[0].CookedValue, 2)
        Available_Memory_MB = [math]::Round($mem.CounterSamples[0].CookedValue, 2)
        Disk_Bytes_Sec = [math]::Round($disk.CounterSamples[0].CookedValue, 2)
    }
    
    Start-Sleep -Milliseconds $IntervalMs
}

$metrics | Export-Csv -Path $OutputFile -NoTypeInformation
Write-Host "Metrics saved to $OutputFile"

3.3网络监控

# Install Wireshark for detailed packet analysis
winget install WiresharkFoundation.Wireshark

# Or use built-in netstat for connection monitoring
netstat -an | findstr "ESTABLISHED"

3.4过程特定监控

# Save as: C:\MCP_Benchmarks\process_monitor.ps1

param(
    [string]$ProcessName,
    [int]$DurationSeconds = 60,
    [string]$OutputFile = "process_metrics.csv"
)

$metrics = @()
$endTime = (Get-Date).AddSeconds($DurationSeconds)

while ((Get-Date) -lt $endTime) {
    $process = Get-Process -Name $ProcessName -ErrorAction SilentlyContinue
    
    if ($process) {
        $metrics += [PSCustomObject]@{
            Timestamp = Get-Date -Format "yyyy-MM-dd HH:mm:ss.fff"
            CPU_Time_Seconds = $process.CPU
            Working_Set_MB = [math]::Round($process.WorkingSet64 / 1MB, 2)
            Private_Memory_MB = [math]::Round($process.PrivateMemorySize64 / 1MB, 2)
            Thread_Count = $process.Threads.Count
            Handle_Count = $process.HandleCount
        }
    }
    
    Start-Sleep -Milliseconds 500
}

$metrics | Export-Csv -Path $OutputFile -NoTypeInformation

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4.详细的基准测试程序

4.1基准1：代币消费分析

设置：创建测试MCP服务器

传统服务器（详细响应）：

# Save as: C:\MCP_Benchmarks\servers\verbose_server.py

from mcp.server import Server
from mcp.types import Tool, TextContent
import mcp.server.stdio
import asyncio
import json

server = Server("verbose-server")

# Simulate large dataset
SAMPLE_DATA = [
    {"id": i, "name": f"Item_{i}", "description": f"Description for item {i} " * 10, 
     "metadata": {"created": "2025-01-01", "modified": "2025-01-05", "tags": ["tag1", "tag2", "tag3"]}}
    for i in range(100)
]

@server.list_tools()
async def list_tools():
    return [
        Tool(
            name="get_all_data",
            description="Returns ALL data items with full details",
            inputSchema={"type": "object", "properties": {}}
        )
    ]

@server.call_tool()
async def call_tool(name: str, arguments: dict):
    if name == "get_all_data":
        # Return everything (inefficient - high token count)
        return [TextContent(type="text", text=json.dumps(SAMPLE_DATA, indent=2))]

async def main():
    async with mcp.server.stdio.stdio_server() as (read_stream, write_stream):
        await server.run(read_stream, write_stream)

if __name__ == "__main__":
    asyncio.run(main())

优化服务器（渐进式披露）：

# Save as: C:\MCP_Benchmarks\servers\optimized_server.py

from mcp.server import Server
from mcp.types import Tool, TextContent
import mcp.server.stdio
import asyncio
import json

server = Server("optimized-server")

# Same dataset
SAMPLE_DATA = [
    {"id": i, "name": f"Item_{i}", "description": f"Description for item {i} " * 10,
     "metadata": {"created": "2025-01-01", "modified": "2025-01-05", "tags": ["tag1", "tag2", "tag3"]}}
    for i in range(100)
]

@server.list_tools()
async def list_tools():
    return [
        Tool(
            name="get_summary",
            description="Returns summary statistics only",
            inputSchema={"type": "object", "properties": {}}
        ),
        Tool(
            name="get_item",
            description="Returns a specific item by ID",
            inputSchema={
                "type": "object",
                "properties": {"id": {"type": "integer"}},
                "required": ["id"]
            }
        ),
        Tool(
            name="search_items",
            description="Search items with filters, returns IDs only",
            inputSchema={
                "type": "object",
                "properties": {
                    "query": {"type": "string"},
                    "limit": {"type": "integer", "default": 10}
                }
            }
        )
    ]

@server.call_tool()
async def call_tool(name: str, arguments: dict):
    if name == "get_summary":
        # Return only summary (efficient - low token count)
        summary = {
            "total_items": len(SAMPLE_DATA),
            "id_range": f"{SAMPLE_DATA[0]['id']}-{SAMPLE_DATA[-1]['id']}",
            "sample_names": [d["name"] for d in SAMPLE_DATA[:3]]
        }
        return [TextContent(type="text", text=json.dumps(summary))]
    
    elif name == "get_item":
        item_id = arguments.get("id", 0)
        item = next((d for d in SAMPLE_DATA if d["id"] == item_id), None)
        return [TextContent(type="text", text=json.dumps(item) if item else "Not found")]
    
    elif name == "search_items":
        limit = arguments.get("limit", 10)
        # Return only IDs
        ids = [d["id"] for d in SAMPLE_DATA[:limit]]
        return [TextContent(type="text", text=json.dumps({"matching_ids": ids}))]

async def main():
    async with mcp.server.stdio.stdio_server() as (read_stream, write_stream):
        await server.run(read_stream, write_stream)

if __name__ == "__main__":
    asyncio.run(main())

令牌计数脚本

# Save as: C:\MCP_Benchmarks\token_counter.py

import tiktoken
import json

def count_tokens(text: str, model: str = "cl100k_base") -> int:
    """Count tokens using tiktoken (GPT-4/Claude approximation)"""
    encoding = tiktoken.get_encoding(model)
    return len(encoding.encode(text))

def analyze_response(response_file: str):
    with open(response_file, 'r') as f:
        data = json.load(f)
    
    text = json.dumps(data)
    tokens = count_tokens(text)
    
    print(f"Response size: {len(text)} characters")
    print(f"Token count: {tokens}")
    print(f"Estimated cost (at $0.01/1K tokens): ${tokens * 0.01 / 1000:.4f}")
    
    return tokens

if __name__ == "__main__":
    import sys
    if len(sys.argv) > 1:
        analyze_response(sys.argv[1])

运行令牌基准测试

# Install tiktoken
pip install tiktoken

# Test verbose server response
python -c "
import json
data = [{'id': i, 'name': f'Item_{i}', 'description': f'Description for item {i} ' * 10} for i in range(100)]
with open('verbose_response.json', 'w') as f:
    json.dump(data, f, indent=2)
"

# Test optimized server response
python -c "
import json
summary = {'total_items': 100, 'id_range': '0-99', 'sample_names': ['Item_0', 'Item_1', 'Item_2']}
with open('optimized_response.json', 'w') as f:
    json.dump(summary, f)
"

# Count tokens
python token_counter.py verbose_response.json
python token_counter.py optimized_response.json

4.2基准2：传输延迟比较

STDIO传输服务器

# Save as: C:\MCP_Benchmarks\servers\stdio_latency_server.py

from mcp.server import Server
from mcp.types import Tool, TextContent
import mcp.server.stdio
import asyncio
import time

server = Server("stdio-latency-test")

@server.list_tools()
async def list_tools():
    return [
        Tool(
            name="ping",
            description="Simple ping for latency measurement",
            inputSchema={"type": "object", "properties": {"timestamp": {"type": "number"}}}
        )
    ]

@server.call_tool()
async def call_tool(name: str, arguments: dict):
    if name == "ping":
        client_time = arguments.get("timestamp", 0)
        server_time = time.time() * 1000
        return [TextContent(type="text", text=f'{{"server_time": {server_time}, "client_time": {client_time}}}')]

async def main():
    async with mcp.server.stdio.stdio_server() as (read_stream, write_stream):
        await server.run(read_stream, write_stream)

if __name__ == "__main__":
    asyncio.run(main())

HTTP传输服务器

# Save as: C:\MCP_Benchmarks\servers\http_latency_server.py

from fastapi import FastAPI, Request
from fastapi.responses import JSONResponse
import time
import uvicorn

app = FastAPI()

@app.post("/mcp/tools/ping")
async def ping(request: Request):
    data = await request.json()
    client_time = data.get("timestamp", 0)
    server_time = time.time() * 1000
    
    return JSONResponse({
        "result": {
            "server_time": server_time,
            "client_time": client_time,
            "processing_time_ms": server_time - client_time
        }
    })

@app.get("/mcp/tools")
async def list_tools():
    return {"tools": [{"name": "ping", "description": "Latency test"}]}

if __name__ == "__main__":
    uvicorn.run(app, host="127.0.0.1", port=8080)

延迟基准客户端

# Save as: C:\MCP_Benchmarks\latency_benchmark.py

import asyncio
import aiohttp
import subprocess
import time
import statistics
import json

async def benchmark_http(url: str, iterations: int = 100):
    """Benchmark HTTP transport latency"""
    latencies = []
    
    async with aiohttp.ClientSession() as session:
        for i in range(iterations):
            start = time.perf_counter()
            async with session.post(
                f"{url}/mcp/tools/ping",
                json={"timestamp": time.time() * 1000}
            ) as response:
                await response.json()
            end = time.perf_counter()
            latencies.append((end - start) * 1000)  # Convert to ms
    
    return latencies

def benchmark_stdio(server_script: str, iterations: int = 100):
    """Benchmark STDIO transport latency"""
    latencies = []
    
    # Start server process
    process = subprocess.Popen(
        ["python", server_script],
        stdin=subprocess.PIPE,
        stdout=subprocess.PIPE,
        stderr=subprocess.PIPE,
        text=True
    )
    
    for i in range(iterations):
        start = time.perf_counter()
        
        # Send JSON-RPC request
        request = json.dumps({
            "jsonrpc": "2.0",
            "id": i,
            "method": "tools/call",
            "params": {"name": "ping", "arguments": {"timestamp": time.time() * 1000}}
        }) + "\n"
        
        process.stdin.write(request)
        process.stdin.flush()
        
        # Read response
        response = process.stdout.readline()
        
        end = time.perf_counter()
        latencies.append((end - start) * 1000)
    
    process.terminate()
    return latencies

def analyze_latencies(latencies: list, name: str):
    """Analyze and report latency statistics"""
    print(f"\n=== {name} Latency Analysis ===")
    print(f"Iterations: {len(latencies)}")
    print(f"Mean: {statistics.mean(latencies):.3f} ms")
    print(f"Median: {statistics.median(latencies):.3f} ms")
    print(f"Std Dev: {statistics.stdev(latencies):.3f} ms")
    print(f"Min: {min(latencies):.3f} ms")
    print(f"Max: {max(latencies):.3f} ms")
    print(f"P95: {sorted(latencies)[int(len(latencies) * 0.95)]:.3f} ms")
    print(f"P99: {sorted(latencies)[int(len(latencies) * 0.99)]:.3f} ms")
    
    return {
        "name": name,
        "mean": statistics.mean(latencies),
        "median": statistics.median(latencies),
        "std": statistics.stdev(latencies),
        "min": min(latencies),
        "max": max(latencies),
        "p95": sorted(latencies)[int(len(latencies) * 0.95)],
        "p99": sorted(latencies)[int(len(latencies) * 0.99)]
    }

async def main():
    results = []
    
    # Benchmark HTTP
    print("Starting HTTP benchmark...")
    http_latencies = await benchmark_http("http://127.0.0.1:8080", iterations=100)
    results.append(analyze_latencies(http_latencies, "HTTP Transport"))
    
    # Save results
    with open("latency_results.json", "w") as f:
        json.dump(results, f, indent=2)
    
    print("\nResults saved to latency_results.json")

if __name__ == "__main__":
    asyncio.run(main())

4.3基准3：序列化开销

# Save as: C:\MCP_Benchmarks\serialization_benchmark.py

import json
import time
import statistics
import sys

# Optional: Install msgpack and protobuf for comparison
# pip install msgpack protobuf

def benchmark_json_serialization(data: dict, iterations: int = 10000):
    """Benchmark JSON serialization/deserialization"""
    
    # Serialization
    serialize_times = []
    for _ in range(iterations):
        start = time.perf_counter()
        serialized = json.dumps(data)
        serialize_times.append(time.perf_counter() - start)
    
    # Deserialization
    deserialize_times = []
    json_str = json.dumps(data)
    for _ in range(iterations):
        start = time.perf_counter()
        json.loads(json_str)
        deserialize_times.append(time.perf_counter() - start)
    
    return {
        "format": "JSON",
        "size_bytes": len(json_str.encode('utf-8')),
        "serialize_mean_us": statistics.mean(serialize_times) * 1_000_000,
        "deserialize_mean_us": statistics.mean(deserialize_times) * 1_000_000,
    }

def benchmark_msgpack_serialization(data: dict, iterations: int = 10000):
    """Benchmark MessagePack serialization"""
    try:
        import msgpack
    except ImportError:
        return {"format": "MessagePack", "error": "Not installed"}
    
    serialize_times = []
    for _ in range(iterations):
        start = time.perf_counter()
        serialized = msgpack.packb(data)
        serialize_times.append(time.perf_counter() - start)
    
    deserialize_times = []
    packed = msgpack.packb(data)
    for _ in range(iterations):
        start = time.perf_counter()
        msgpack.unpackb(packed)
        deserialize_times.append(time.perf_counter() - start)
    
    return {
        "format": "MessagePack",
        "size_bytes": len(packed),
        "serialize_mean_us": statistics.mean(serialize_times) * 1_000_000,
        "deserialize_mean_us": statistics.mean(deserialize_times) * 1_000_000,
    }

def create_test_payloads():
    """Create test payloads of varying complexity"""
    
    # Small payload (simple tool call)
    small = {
        "jsonrpc": "2.0",
        "id": 1,
        "method": "tools/call",
        "params": {"name": "get_file", "arguments": {"path": "/test.txt"}}
    }
    
    # Medium payload (tool response with data)
    medium = {
        "jsonrpc": "2.0",
        "id": 1,
        "result": {
            "content": [
                {"type": "text", "text": "x" * 1000}  # 1KB of text
            ],
            "metadata": {
                "tokens": 150,
                "model": "claude-3",
                "timestamp": "2025-01-05T12:00:00Z"
            }
        }
    }
    
    # Large payload (tool definitions)
    large = {
        "jsonrpc": "2.0",
        "id": 1,
        "result": {
            "tools": [
                {
                    "name": f"tool_{i}",
                    "description": f"Description for tool {i} " * 20,
                    "inputSchema": {
                        "type": "object",
                        "properties": {
                            f"param_{j}": {"type": "string", "description": f"Parameter {j}"}
                            for j in range(10)
                        }
                    }
                }
                for i in range(50)  # 50 tools
            ]
        }
    }
    
    return {"small": small, "medium": medium, "large": large}

def main():
    payloads = create_test_payloads()
    results = []
    
    for size_name, payload in payloads.items():
        print(f"\n=== Benchmarking {size_name.upper()} payload ===")
        
        json_result = benchmark_json_serialization(payload)
        json_result["payload_size"] = size_name
        results.append(json_result)
        print(f"JSON: {json_result['size_bytes']} bytes, "
              f"serialize: {json_result['serialize_mean_us']:.2f}µs, "
              f"deserialize: {json_result['deserialize_mean_us']:.2f}µs")
        
        msgpack_result = benchmark_msgpack_serialization(payload)
        if "error" not in msgpack_result:
            msgpack_result["payload_size"] = size_name
            results.append(msgpack_result)
            print(f"MessagePack: {msgpack_result['size_bytes']} bytes, "
                  f"serialize: {msgpack_result['serialize_mean_us']:.2f}µs, "
                  f"deserialize: {msgpack_result['deserialize_mean_us']:.2f}µs")
            
            # Calculate overhead
            overhead = (json_result['size_bytes'] - msgpack_result['size_bytes']) / msgpack_result['size_bytes'] * 100
            print(f"JSON overhead vs MessagePack: {overhead:.1f}%")
    
    # Save results
    with open("serialization_results.json", "w") as f:
        json.dump(results, f, indent=2)
    
    print("\nResults saved to serialization_results.json")

if __name__ == "__main__":
    main()

4.4基准4：缓存有效性

# Save as: C:\MCP_Benchmarks\caching_benchmark.py

import time
import statistics
import json
from functools import lru_cache
import hashlib

# Simulated expensive operation
def expensive_database_query(query: str) -> dict:
    """Simulate expensive database query (100ms)"""
    time.sleep(0.1)  # Simulate latency
    return {
        "query": query,
        "results": [{"id": i, "data": f"result_{i}"} for i in range(10)],
        "timestamp": time.time()
    }

# In-memory cache implementation
class SimpleCache:
    def __init__(self, ttl_seconds: int = 60):
        self._cache = {}
        self._ttl = ttl_seconds
    
    def _hash_key(self, key: str) -> str:
        return hashlib.md5(key.encode()).hexdigest()
    
    def get(self, key: str):
        hashed = self._hash_key(key)
        if hashed in self._cache:
            value, timestamp = self._cache[hashed]
            if time.time() - timestamp  dict:
    """Query with caching"""
    cached_result = cache.get(query)
    if cached_result:
        return cached_result
    
    result = expensive_database_query(query)
    cache.set(query, result)
    return result

def benchmark_without_cache(queries: list, iterations: int = 5):
    """Benchmark without caching"""
    times = []
    for _ in range(iterations):
        for query in queries:
            start = time.perf_counter()
            expensive_database_query(query)
            times.append(time.perf_counter() - start)
    return times

def benchmark_with_cache(queries: list, iterations: int = 5):
    """Benchmark with caching"""
    cache._cache.clear()  # Clear cache
    times = []
    
    for iteration in range(iterations):
        for query in queries:
            start = time.perf_counter()
            cached_query(query)
            times.append(time.perf_counter() - start)
    
    return times

def main():
    # Test queries (some repeated to test cache hits)
    queries = [
        "SELECT * FROM users WHERE id = 1",
        "SELECT * FROM users WHERE id = 2",
        "SELECT * FROM users WHERE id = 1",  # Repeat
        "SELECT * FROM orders WHERE user_id = 1",
        "SELECT * FROM users WHERE id = 1",  # Repeat
        "SELECT * FROM products WHERE category = 'electronics'",
        "SELECT * FROM orders WHERE user_id = 1",  # Repeat
    ]
    
    print("=== Caching Effectiveness Benchmark ===\n")
    
    # Benchmark without cache
    print("Running without cache...")
    no_cache_times = benchmark_without_cache(queries, iterations=3)
    
    # Benchmark with cache
    print("Running with cache...")
    with_cache_times = benchmark_with_cache(queries, iterations=3)
    
    # Analyze results
    print("\n=== Results ===")
    print(f"\nWithout Cache:")
    print(f"  Total time: {sum(no_cache_times)*1000:.2f} ms")
    print(f"  Mean per query: {statistics.mean(no_cache_times)*1000:.2f} ms")
    
    print(f"\nWith Cache:")
    print(f"  Total time: {sum(with_cache_times)*1000:.2f} ms")
    print(f"  Mean per query: {statistics.mean(with_cache_times)*1000:.2f} ms")
    
    # Calculate improvement
    improvement = (sum(no_cache_times) - sum(with_cache_times)) / sum(no_cache_times) * 100
    speedup = sum(no_cache_times) / sum(with_cache_times)
    
    print(f"\n=== Improvement ===")
    print(f"  Time reduction: {improvement:.1f}%")
    print(f"  Speedup factor: {speedup:.1f}x")
    
    # Cache statistics
    unique_queries = len(set(queries))
    total_queries = len(queries) * 3  # iterations
    cache_hits = total_queries - unique_queries * 3
    hit_rate = cache_hits / total_queries * 100
    
    print(f"\n=== Cache Statistics ===")
    print(f"  Unique queries: {unique_queries}")
    print(f"  Total queries: {total_queries}")
    print(f"  Estimated cache hits: {cache_hits}")
    print(f"  Hit rate: {hit_rate:.1f}%")
    
    # Save results
    results = {
        "without_cache": {
            "total_ms": sum(no_cache_times) * 1000,
            "mean_ms": statistics.mean(no_cache_times) * 1000
        },
        "with_cache": {
            "total_ms": sum(with_cache_times) * 1000,
            "mean_ms": statistics.mean(with_cache_times) * 1000
        },
        "improvement_percent": improvement,
        "speedup_factor": speedup,
        "cache_hit_rate": hit_rate
    }
    
    with open("caching_results.json", "w") as f:
        json.dump(results, f, indent=2)

if __name__ == "__main__":
    main()

4.5基准5：能耗计量

# Save as: C:\MCP_Benchmarks\energy_benchmark.py

import subprocess
import time
import json
import os
import statistics

def get_cpu_power_estimate():
    """
    Estimate CPU power using Windows Performance Counters
    Note: For accurate measurements, use Intel Power Gadget or HWiNFO
    """
    try:
        result = subprocess.run(
            ['powershell', '-Command', 
             "(Get-Counter '\\Processor(_Total)\\% Processor Time').CounterSamples[0].CookedValue"],
            capture_output=True, text=True, timeout=5
        )
        cpu_percent = float(result.stdout.strip())
        
        # Rough estimate: Assume TDP of 65W, scale by usage
        # This is a ROUGH estimate - use proper power monitoring tools for accuracy
        tdp_watts = 65  # Adjust based on your CPU
        estimated_power = (cpu_percent / 100) * tdp_watts
        
        return cpu_percent, estimated_power
    except Exception as e:
        return None, None

def run_workload(workload_func, duration_seconds: int = 30):
    """Run a workload and measure power consumption"""
    measurements = []
    
    start_time = time.time()
    end_time = start_time + duration_seconds
    
    # Start workload in background
    import threading
    workload_running = True
    
    def workload_thread():
        while workload_running:
            workload_func()
    
    thread = threading.Thread(target=workload_thread)
    thread.start()
    
    # Collect measurements
    while time.time()  Protocol Hierarchy

______________________________________________________________________

10.论文提交清单

\[\]运行所有基准测试至少3次
\[\]文件系统规范
\[\]计算统计显著性（比较组的p值）
\[\]生成出版物质量数据（300 DPI，PDF格式）
\[\]用实证验证部分更新论文
\[\]将基准方法添加到再现性附录中
\[\]在你的论文中引用这种基准测试方法
\[\]提供基准代码（GitHub存储库）

______________________________________________________________________

联系和支持

有关此基准测试指南的问题，请咨询：

MCP SDK文档：https://modelcontextprotocol.io
Python MCP：https://github.com/modelcontextprotocol/python-sdk
TypeScript MCP：https://github.com/modelcontextprotocol/typescript-sdk

MCP Server Benchmarks

快速接入

详细介绍

绿色计算研究MCP服务器基准测试指南

概述

目录

1.环境设置

1.1硬件要求文件

1.2软件先决条件

安装Node.js（用于JavaScript MCP服务器）

安装Python（适用于Python MCP服务器）

安装Docker（用于容器化基准测试）

安装UV（MCP的快速Python包管理器）

1.3 MCP SDK安装

1.4安装克劳德桌面（MCP主机）

2.基准情景

场景1：代币消费比较

场景2：传输机制延迟

场景3：序列化开销

场景4：缓存效率

场景5：资源利用

场景6：多服务器可扩展性

3.测量工具

3.1能源计量工具

Windows内置电源监控

英特尔电源小工具（适用于英特尔CPU）

HWiNFO64（综合硬件监控）

打开硬件监视器（CPU/GPU电源）

3.2性能监控工具

自定义PowerShell监控脚本

3.3网络监控

3.4过程特定监控

4.详细的基准测试程序

4.1基准1：代币消费分析

设置：创建测试MCP服务器

令牌计数脚本

运行令牌基准测试

4.2基准2：传输延迟比较

STDIO传输服务器

HTTP传输服务器

延迟基准客户端

4.3基准3：序列化开销

4.4基准4：缓存有效性

4.5基准5：能耗计量

10.论文提交清单

联系和支持

目录标签

接入字段

权限和风险

来源信息

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