MockLoop

MockLoop is an MCP server that provides an AI-native API testing and mocking platform directly within Model Context Protocol environments. It connects AI assistants like Claude Desktop and Cline to automated testing routines, mock servers, and scenario workflows. Software developers, QA engineers, and API designers use MockLoop to analyze API specifications, deploy dynamic mock endpoints, execute performance tests, and monitor validation outcomes. The platform operates using a dual-port architecture that separates the mocked API from administration controls, avoiding routing conflicts. By supplying specialized testing tools, context-management utilities, and prompt templates, the server enables automated scenario validation, load generation, security vulnerability checks, and stateful multi-step API workflows. It also supports test session contexts, snapshots, and global state tracking across test runs, allowing agents to systematically assess complex back-end architectures, track regressions over time, and produce structured testing reports without manual test script creation.

Category: Developer Tools & Code Intelligence

Tags: api testing, mocking, qa, test automation

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How to install and configure MockLoop

1. Ensure Python 3.10 or higher and pip are installed. 2. Install the package from PyPI by running pip install mockloop-mcp. 3. Verify the installation by running mockloop-mcp --version in your terminal. 4. To configure Claude Desktop, edit your claude_desktop_config.json file and add the server definition: json { "mcpServers": { "mockloop": { "command": "mockloop-mcp", "args": [] } } } 5. Alternatively, for clients like Cline, configure the server in your MCP settings with command set to mockloop-mcp, args as [], and transportType set to stdio. 6. Restart your MCP client to load the tools and prompts.

What you can do with MockLoop

• Analyze OpenAPI specifications using specialized MCP prompts to construct automated test plans and mock scenario definitions. * Deploy and dynamically switch mock API scenarios during integration testing to simulate production conditions without touching live back ends. * Execute load testing routines and security vulnerability scans against API endpoints, capturing performance metrics directly through client prompts. * Manage stateful API testing sessions using dedicated context tools to maintain variables, snapshots, and rollbacks across multi-step agent workflows. * Compare results across multiple testing iterations to identify API regressions, response variations, and performance bottlenecks automatically.

Key facts

• https://github.com/MockLoop/mockloop-mcp
• Developer Tools & Code Intelligence, Monitoring & Observability
• api testing, mocking, qa, test automation

Part of MCP Servers

How do I install MockLoop?

You can install MockLoop directly from PyPI using pip by running pip install mockloop-mcp in a Python 3.10 or newer environment. You can then verify the setup by running mockloop-mcp --version before configuring your preferred MCP client.

Which MCP clients work with MockLoop?

MockLoop works with any MCP-compatible environment that supports stdio transport. Common examples documented by the project include Claude Desktop and the Cline extension for VS Code.

What can MockLoop do?

MockLoop provides tools and prompts for API mocking and test automation. It allows AI agents to analyze OpenAPI specs, deploy mock scenarios, run load and security tests, capture context snapshots, and generate detailed test reports.

What prerequisites are needed to run MockLoop?

Running MockLoop requires Python 3.10 or higher, the pip package manager, an MCP-compatible client, and Docker with Docker Compose if you intend to run containerized mock servers.