MockLoop MCP - AI-Native Testing Platform

Name: MockLoop MCP - AI-Native Testing Platform
Author: MockLoop

@MockLoop

关于 MockLoop MCP - AI-Native Testing Platform

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

基本信息

分类

其他

许可证

MIT

运行时

python

传输方式

stdio

发布者

MockLoop

配置

使用下面的配置,将此服务器添加到你的 MCP 客户端。

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

工具

未检测到工具

工具是从 README 中自动提取的。维护者可以在 ## Tools 标题下列出工具,即可填充这部分内容。

概览

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

FAQ from MockLoop MCP - AI-Native Testing Platform

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).

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.

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.

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).

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.