MCP Scoring Engine

mcp评分引擎

一个独立的评分引擎，用于评估 模型上下文协议（MCP） 服务器。纯Python，没有框架依赖关系——只有数据类、评分逻辑和网络探测。

用于生产 MCP记分牌 对数千台MCP服务器进行分级。

安装

pip install mcp-scoring-engine

快速开始

从GitHub仓库中对服务器进行评分（静态分析）

from mcp_scoring_engine import ServerInfo, analyze_repo, compute_score

server = ServerInfo(
    name="my-mcp-server",
    description="A tool server for doing useful things",
    repo_url="https://github.com/owner/my-mcp-server",
)

static = analyze_repo(server.repo_url)
result = compute_score(server, static_result=static)

print(result.composite_score)  # 0–100
print(result.grade)            # "A+", "B", "D", etc.
print(result.score_type)       # "partial" (1 tier) or "full" (2+ tiers)

探测正在运行的服务器

from mcp_scoring_engine import (
    ServerInfo, probe_server, deep_probe_server, compute_score
)

# Fast health check (~10s) — connection, initialize, ping
fast = probe_server("https://my-server.example.com/mcp")
print(fast.is_reachable, fast.connection_ms)

# Deep protocol probe (~30s) — schema validation, error handling, fuzz testing
deep = deep_probe_server("https://my-server.example.com/mcp")
print(deep.tools_count, deep.schema_valid, deep.fuzz_score)

# Score with the probe results
server = ServerInfo(name="my-server", description="...", repo_url="...")
static = analyze_repo(server.repo_url)
result = compute_score(server, static_result=static, deep_probe=deep)
print(result.grade)

探测stdio服务器

from mcp_scoring_engine import probe_server_stdio, deep_probe_server_stdio

fast = probe_server_stdio(["npx", "-y", "@modelcontextprotocol/server-memory"])
deep = deep_probe_server_stdio(["python", "-m", "my_mcp_server"])

对服务器进行分类

from mcp_scoring_engine import classify_server, ServerInfo

server = ServerInfo(
    name="stripe-mcp",
    description="MCP server for Stripe payment processing",
    repo_url="https://github.com/stripe/stripe-mcp",
)

category, targets = classify_server(server)
print(category)  # "finance"
print(targets)   # ["Stripe"]

检测stdio服务器的入口点

from mcp_scoring_engine import detect_entry_point, make_github_file_reader

# With a GitHubPublicClient (from your own GitHub API code)
file_reader = make_github_file_reader(client)
tree = client.get_tree()

result = detect_entry_point(tree, file_reader)
# {"language": "python", "run_cmd": ["python", "-m", "my_server"],
#  "install_cmd": "uv pip install -e .",
#  "source": "pyproject.toml [project.scripts]", "confidence": "high"}

入口点检测解析构建元数据以推断如何运行MCP服务器：

• python: pyproject.toml 脚本， setup.cfg/setup.py console_scripts， __main__.py
• 节点: package.json bin字段， scripts.start, main 领域

当通过拨打电话时 analyze_repo()，检测以零额外API成本搭载在已提取的文件树上。结果存储在 StaticAnalysis.details["entry_point"].

检测危险信号

from mcp_scoring_engine import detect_flags, ServerInfo

server = ServerInfo(
    name="sketchy-server",
    description="A MCP server",
    repo_url="",
    remote_endpoint_url="http://localhost:3000/mcp",
)

flags = detect_flags(server)
for flag in flags:
    print(f"[{flag.severity}] {flag.label}: {flag.description}")
    # [critical] No Source Code: No repository URL or source link provided
    # [warning] Staging Artifact: Endpoint URL contains localhost or staging reference

建筑

引擎评估跨服务器 三个数据层:

综合得分是五个类别的加权混合：

分数类型：

• partial --只有1个数据层可用。数字分数，但没有字母等级。
• full --2+数据层。A+至F级。

API 参考

核心

探针

类型

所有输入和输出都是纯数据类：

• ServerInfo --输入服务器元数据（名称、描述、repo_url等）
• ScoreResult --完整的评分输出（综合得分、等级、类别得分、旗帜、徽章）
• FastProbeResult --健康检查结果（可获取，计时）
• DeepProbeResult --协议合规性结果（模式、错误处理、模糊）
• StaticAnalysis --回购分析结果（7个指标得分+GitHub元数据）
• ReliabilityData --预先计算的可靠性指标（正常运行时间、延迟）
• Flag --危险信号（关键、严重性、标签、描述）
• Badge --显示徽章（钥匙、标签、级别）

许可证

麻省理工学院