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MockLoop MCP - AI-Native Testing Platform

@MockLoop

基本情報

カテゴリ

その他

ライセンス

MIT

トランスポート

stdio

作者

MockLoop

MockLoop MCP - AI-Native Testing Platform について

Intelligent Model Context Protocol (MCP) server for AI-assisted API development. Generate mock servers from OpenAPI specs with advanced logging, performance analytics, and server discovery. Optimized for AI development workflows with comprehensive testing insights and automated a

#training#mock#api#ai#mcp#models#swagger#openapi#mocking-utility#feedback-loop#mocking-server#training-data#llm#mcp-servers#mcp-server

MockLoop MCP - AI-Native Testing Platform の設定

以下の設定を使って、このサーバーを MCP 対応クライアントに追加してください。

{
  "mcpServers": {
    "mockloop-mcp": {
      "command": "python3",
      "args": [
        "-m",
        "venv",
        ".venv"
      ]
    }
  }
}

MockLoop MCP - AI-Native Testing Platform のツール

ツール一覧はまだありません

この掲載には表示できるツール一覧がまだありません。サーバーにツールがないという意味ではありません。対応機能はプロジェクトのドキュメントをご確認ください。

ツールは README から自動的に抽出されます。メンテナーは ## Tools という見出しの下に記載することで、このタブに反映できます。

概要

What is MockLoop MCP - AI-Native Testing Platform?

MockLoop MCP is an AI-native API testing platform powered by the Model Context Protocol (MCP). It provides comprehensive AI‑driven scenario generation, automated test execution, and intelligent analysis capabilities for developers and QA teams.

How to use MockLoop MCP - AI-Native Testing Platform?

Install the Python package from PyPI (pip install mockloop-mcp). Ensure you have Python 3.10+, Docker, and an MCP‑compatible client (e.g., Cline, Claude Desktop). Configure the server as a stdio MCP server by adding the appropriate JSON block to your client’s MCP settings file.

Key features of MockLoop MCP - AI-Native Testing Platform

  • AI‑driven test generation with 5 specialized MCP prompts
  • 15 community‑driven scenario packs for load, error, security, and performance testing
  • 16 automated testing tools for scenario management, execution, analysis, and workflow
  • 10 stateful context management tools for complex workflow orchestration
  • Dual‑port architecture (mocked API port 8000, admin UI port 8001)
  • Enterprise‑grade audit logging and regulatory compliance tracking

Use cases of MockLoop MCP - AI-Native Testing Platform

  • Generate and deploy mock API servers from OpenAPI specifications
  • Execute automated load tests and security vulnerability assessments
  • Create and manage stateful testing workflows with context snapshots
  • Analyze test results, compare runs, and produce compliance reports
  • Simulate error conditions and performance benchmarks for regression testing

よくある質問

1

What prerequisites are needed to run MockLoop MCP?

Python 3.10+, pip package manager, Docker and Docker Compose (for containerized mock servers), and an MCP‑compatible client (e.g., Cline, Claude Desktop).

2

How do I install MockLoop MCP?

Install the latest stable version from PyPI: `pip install mockloop-mcp`. Optional extras include `[dev]`, `[docs]`, and `[all]`. Verify with `mockloop-mcp --version`.

3

How do I configure MockLoop MCP with my MCP client?

Add the server entry to your client’s MCP settings using the `stdio` transport. For Cline: `"command": "mockloop-mcp"`. For Claude Desktop: `"command": "mockloop-mcp"`. For virtual environments, use the full Python path and `-m mockloop_mcp`.

4

What tools does MockLoop MCP expose?

It exposes 30 MCP tools: 4 core (e.g., `generate_mock_api`, `query_mock_logs`), 16 testing tools (scenario management, test execution, analysis/reporting, workflow), and 10 context management tools (session, workflow, agent, snapshot, global context).

5

Does MockLoop MCP support stateful testing?

Yes. It provides advanced context management with session, workflow, and agent contexts, including snapshot creation and restoration for state rollback and cross‑session data sharing.

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