MCP Gatekeeper

MCP Gatekeeper

Enables users to validate MCP servers, skills, extensions, and packages for schema, security, functional, and semantic quality directly from their MCP client.

Category
访问服务器

README

<p align="center"> <h1 align="center">MCP Gatekeeper</h1> <p align="center"> <strong>The QA framework for the MCP ecosystem</strong> <br /> 46 validators · 8 gates · Schema · Security · Functional · AI Eval · Human Review </p> </p>

<p align="center"> <a href="#quick-start">Quick Start</a> · <a href="#what-it-catches">What It Catches</a> · <a href="#gates">Gates</a> · <a href="#ai-evaluation">AI Evaluation</a> · <a href="#use-as-mcp-server">Use as MCP Server</a> </p>


MCP has 200+ server implementations but zero quality gates before publishing. MCP Gatekeeper fills that gap.

Run it before you publish to the MCP Registry, npm, or Smithery. It catches schema violations, security vulnerabilities, functional bugs, and quality issues that other tools miss — including an LLM-powered semantic evaluation that compares what your tools claim to do against what they actually do.

npx mcpqa run --server-url https://your-mcp-server.com/mcp

What It Catches

Real findings from running against a production Zoho MCP server (65 tools):

Finding Gate What It Means
125 tools accept invalid inputs silently Functional Server returns isError: false for empty/wrong-type args
107 parameters have no description Schema LLMs can't use tools with undocumented params
26 write tools lack rate limit info Security Consumers don't know if there's a throttle
SQL injection payloads reflected in output Security Dynamic injection testing caught real vulnerability
Tool descriptions don't match actual behavior AI Eval Claude compared descriptions vs invocation results

Quick Start

# Validate an MCP server (Gates 1-3: schema + security + functional)
npx mcpqa run --server-url https://your-server.com/mcp

# Add AI semantic evaluation (Gate 4 — requires Anthropic API key)
ANTHROPIC_API_KEY=... npx mcpqa run --server-url https://your-server.com/mcp --gates 1,2,3,4

# Validate a Claude Code skill
npx mcpqa run --skill-path ./.claude/skills/my-skill --gates 6

# Validate an extension
npx mcpqa run --extension-path ./my-extension --gates 7

# Validate your npm package before publishing
npx mcpqa run --package-path . --gates 8

# Everything at once
npx mcpqa run --server-url https://server.com/mcp --package-path . --gates 1,2,3,4,8 --mode lenient

Gates

Gate What It Validates Validators Speed
1. Schema Protocol conformance, tool names, JSON Schema, descriptions, parameter types 6 < 1s
2. Security Prompt injection, SSRF, secrets, permissions, dangerous patterns, dynamic injection with real payloads 9 2-10s
3. Functional Handshake, invocation, boundary testing, error format, timeouts, idempotency 9 2-5min
4. AI Semantic Description accuracy vs behavior, param clarity, response completeness, integration readiness, tool chains 7 2-5min
5. Human Review Dashboard with approve/reject/escalate, auto-approve for high scores, audit trail 1 On-demand
6. Skills SKILL.md structure, frontmatter, description quality, content length, references, scripts 6 < 1s
7. Extensions Manifest, permissions, bundled MCP configs, security scanning 4 < 1s
8. Package package.json, server.json, LICENSE, dependency audit, secrets in source 5 < 1s

AI Evaluation

Gate 4 uses an LLM-as-judge (Claude or OpenAI-compatible) to evaluate things code can't:

  • Description accuracy: Does the description match what the tool actually does? (compares against Gate 3 invocation results)
  • Parameter clarity: Could a developer use this tool correctly on the first try?
  • Integration readiness: Can someone build an integration from metadata alone?
  • Tool chain analysis: Do the tools form coherent workflows, or are there gaps?

Applies patterns from Anthropic's harness design and eval framework:

  • Skeptical evaluator — system prompt forces adversarial QA, not praise
  • pass@k / pass^k — multi-trial evaluation for reliability metrics
  • Actionable fixes — every finding includes specific developer guidance
  • Transcript recording — full LLM conversations saved for debugging

Use as MCP Server

MCP Gatekeeper is itself an MCP server. Add it to Claude Desktop:

{
  "mcpServers": {
    "mcpqa": {
      "command": "npx",
      "args": ["-y", "mcpqa-server"]
    }
  }
}

Then ask Claude: "Validate the MCP server at https://example.com/mcp"

6 tools exposed: validate_mcp_server, validate_skill, validate_extension, validate_package, get_report, list_validators

CLI Reference

mcpqa run [options]          # Run validation pipeline
mcpqa validate-config <path> # Validate YAML config
mcpqa diff <before> <after>  # Compare two reports
mcpqa dashboard [--port N]   # Launch human review UI

Options:
  --server-url <url>         MCP server URL (http/sse)
  --server-cmd <cmd>         MCP server command (stdio)
  --skill-path <path>        Claude Code skill directory
  --extension-path <path>    Extension directory
  --package-path <path>      npm package directory
  --gates <nums>             Gate numbers (e.g., 1,2,3,4,6,7,8)
  --mode <strict|lenient>    Stop on first fail vs run all
  --trials <number>          LLM evaluation trials for pass@k
  --save-baseline            Save results for regression tracking
  --check-regression         Compare against saved baseline
  --verbose / --debug        Control log verbosity
  --dry-run                  Show what would run without executing

Reports

Format File Use Case
Console stdout Real-time progress with colored badges and progress bars
JSON reports/latest.json CI/CD integration, programmatic access
HTML reports/latest.html Dark-themed visual report for stakeholders
SARIF reports/latest.sarif GitHub Code Scanning, VS Code SARIF Viewer
Transcripts reports/transcripts/ Gate 4 LLM conversation debugging

CI/CD Integration

# GitHub Actions
- name: Validate MCP Server
  run: npx mcpqa run --server-cmd "node dist/server.js" --gates 1,2,3,8
  # Exit code 0 = pass, 1 = fail
# Docker
docker build -t mcpqa .
docker run mcpqa run --server-url https://server.com/mcp

Configuration

# configs/default.yaml
pipeline:
  mode: strict
  enabledGates: [1, 2, 3]

server:
  transport: http
  url: https://your-server.com/mcp
  headers:
    Authorization: "Bearer your-token"

semantic:
  trials: 3                    # pass@k evaluation
  autoApproveThreshold: 4.5   # Gate 5 auto-approve

reporting:
  formats: [console, json, sarif]
  outputDir: ./reports

Development

npm install
npm test              # 172 tests
npm run lint          # type-check
npm run build         # compile to dist/

Architecture

CLI (mcpqa) ─┐                    ┌─ MCP Server (mcpqa-server)
              │                    │
              ▼                    ▼
         PipelineOrchestrator (shared core)
              │
    ┌─────────┼──────────┬──────────┐
    ▼         ▼          ▼          ▼
 Gate 1-3  Gate 4     Gate 5     Gate 6-8
 Code      LLM Judge  Dashboard  File
 (24 val)  (7 val)    (htmx)    (15 val)

Contributing

PRs welcome. Each validator is a single file implementing IValidator. To add a new check:

  1. Create src/gates/gateN-name/validators/your-check.ts
  2. Implement IValidator interface (name, description, validate)
  3. Register in the gate's index.ts
  4. Add tests in tests/unit/gateN/

License

MIT

Links

推荐服务器

Baidu Map

Baidu Map

百度地图核心API现已全面兼容MCP协议,是国内首家兼容MCP协议的地图服务商。

官方
精选
JavaScript
Playwright MCP Server

Playwright MCP Server

一个模型上下文协议服务器,它使大型语言模型能够通过结构化的可访问性快照与网页进行交互,而无需视觉模型或屏幕截图。

官方
精选
TypeScript
Magic Component Platform (MCP)

Magic Component Platform (MCP)

一个由人工智能驱动的工具,可以从自然语言描述生成现代化的用户界面组件,并与流行的集成开发环境(IDE)集成,从而简化用户界面开发流程。

官方
精选
本地
TypeScript
Audiense Insights MCP Server

Audiense Insights MCP Server

通过模型上下文协议启用与 Audiense Insights 账户的交互,从而促进营销洞察和受众数据的提取和分析,包括人口统计信息、行为和影响者互动。

官方
精选
本地
TypeScript
VeyraX

VeyraX

一个单一的 MCP 工具,连接你所有喜爱的工具:Gmail、日历以及其他 40 多个工具。

官方
精选
本地
graphlit-mcp-server

graphlit-mcp-server

模型上下文协议 (MCP) 服务器实现了 MCP 客户端与 Graphlit 服务之间的集成。 除了网络爬取之外,还可以将任何内容(从 Slack 到 Gmail 再到播客订阅源)导入到 Graphlit 项目中,然后从 MCP 客户端检索相关内容。

官方
精选
TypeScript
Kagi MCP Server

Kagi MCP Server

一个 MCP 服务器,集成了 Kagi 搜索功能和 Claude AI,使 Claude 能够在回答需要最新信息的问题时执行实时网络搜索。

官方
精选
Python
e2b-mcp-server

e2b-mcp-server

使用 MCP 通过 e2b 运行代码。

官方
精选
Neon MCP Server

Neon MCP Server

用于与 Neon 管理 API 和数据库交互的 MCP 服务器

官方
精选
Exa MCP Server

Exa MCP Server

模型上下文协议(MCP)服务器允许像 Claude 这样的 AI 助手使用 Exa AI 搜索 API 进行网络搜索。这种设置允许 AI 模型以安全和受控的方式获取实时的网络信息。

官方
精选