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Mockloop MCP

Runs AI-assisted API testing workflows with scenario management, load and security tests, reporting, and performance analysis.

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Install Mockloop MCP

Pick your client, then add it in one click or paste the config.

STDIOPython
~/Library/Application Support/Claude/claude_desktop_config.json
claude_desktop_config.json
{
  "mcpServers": {
    "mockloop-mcp": {
      "command": "uvx",
      "args": [
        "mockloop-mcp"
      ]
    }
  }
}

Settings → Developer → Edit Config, merge this into mcpServers, then fully quit and reopen Claude Desktop.

Install not yet sandbox-tested— we haven't run this command through our automated sandbox yet. Not a red flag; we're working through the catalog.

Overview

mockloop-mcp provides MCP tools for AI-assisted API testing, including scenario deployment, test execution, load and security testing, and result analysis. It can generate mock servers from OpenAPI specifications and supports logging, performance metrics, server discovery, and testing insights. Stateful contexts, sessions, snapshots, and workflow controls support multi-step testing processes. Reach for it when an MCP client needs to create, run, monitor, and compare API test workflows.

Use cases

  • Generate API testing scenarios from OpenAPI specifications
  • Deploy and switch between mock API scenarios
  • Run load and security tests through an AI client
  • Compare test runs and inspect performance metrics
  • Schedule test suites and monitor their progress

Key features

  • OpenAPI-oriented scenario generation
  • Scenario deployment and switching
  • Load and security test execution
  • Test result analysis and reporting
  • Performance metrics and run comparison
  • Stateful sessions, contexts, and snapshots

Tools16

What Mockloop MCP lets your agent do. Expand a tool for its parameters.

Self-reported~74 tokens of context

Security & health

What it can reach and how reliably it runs — from our own checks, not the maintainer’s claims.

ActiveLast checked Today

Recent health check succeeded.

4/4 checks healthy over the last 12d

Supply-chain signal

No high-severity advisories surfaced by our automated scan.

Critical 0High 0Medium 0Low 0

Scanned 12d ago via OSV.dev · mockloop-mcp (PyPI)

Adoption & maintenance

Factual signals from GitHub, npm, and our automated checks — not a rating.

GitHub stars17Stargazers on the source repository.
Last commit6d agoMost recent push to the default branch.
Tools exposed16Callable tools this server registers over MCP.

How Mockloop MCP works

What mockloop-mcp does

mockloop-mcp connects an MCP-compatible AI client to API testing workflows centered on scenarios, test plans, and test results. Its capabilities cover scenario validation and deployment, switching between active scenarios, individual test iterations, complete test plans, load tests, and security tests.

The server also supports analysis and reporting. Agents can inspect performance metrics, compare test runs, generate reports, and monitor progress. OpenAPI-oriented prompts can help analyze an API for testing, generate scenario configurations, optimize scenarios for load, create error cases, and produce security-test scenarios.

Scenario resources include load-testing, error-simulation, security, performance, and integration-testing packs. The project also describes community sharing and validation for these scenario resources.

How it works

The server exposes testing operations as MCP tools and maintains state for longer workflows. Test sessions can be created and ended explicitly, while workflow and agent contexts hold data across related operations. Context data can be retrieved or updated, and context snapshots can be created and restored for recovery or rollback.

Global context provides a way to share selected data across sessions. The workflow tools can schedule a test suite and report its progress while execution is underway. Audit logging covers request and response trails, performance information, and security events for testing and compliance-oriented tracking.

Setup and configuration

Install the Python package with:

Terminal
pip install mockloop-mcp

Verify the command is available by running:

bash
mockloop-mcp --version

Then add the server to an MCP client configuration. The quick-start instructions identify Cline and Claude Desktop as supported configuration targets. Releases and tool schemas can also be checked through the project’s SchemaPin metadata at https://mockloop.com/.well-known/schemapin.json.

Tools and capabilities

The exposed testing tools are grouped into four areas:

  • Scenario management: validate_scenario_config, deploy_scenario, switch_scenario, and list_active_scenarios.
  • Test execution: execute_test_plan, run_test_iteration, run_load_test, and run_security_test.
  • Analysis and reporting: analyze_test_results, generate_test_report, compare_test_runs, and get_performance_metrics.
  • Workflow management: create_test_session, end_test_session, schedule_test_suite, and monitor_test_progress.

Additional MCP prompts support OpenAPI analysis and scenario generation. Context operations include creating session, workflow, and agent contexts; reading and updating context data; listing contexts; managing snapshots; and reading or changing global context.

Limitations and notes

mockloop-mcp is designed for MCP clients and requires client-side MCP configuration after installation. Its testing behavior depends on the scenarios, test plans, and context supplied to the tools. Validate that installed releases and tool schemas originate from the project’s repository, using SchemaPin where appropriate.

Read the full README View source on GitHub

Reviews

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Frequently Asked Questions about Mockloop MCP

mockloop-mcp is an MCP server that connects AI clients to API testing workflows and mock-server scenarios. Its main tools validate, deploy, and switch scenarios; execute test plans, load tests, and security tests; analyze results; generate reports; compare runs; collect performance metrics; and monitor scheduled test sessions.

Install it with `pip install mockloop-mcp`, then verify the command with `mockloop-mcp --version`. Add the server to your MCP client configuration afterward.

Cursor is not identified as a supported client in the available setup instructions. The quick-start configuration specifically names Cline and Claude Desktop.

Yes, mockloop-mcp exposes `run_load_test` and `run_security_test`. It also provides tools for executing test plans, running individual iterations, analyzing results, and collecting performance metrics.

It provides session, workflow, agent, and global context operations. Agents can update context data, create and restore snapshots, share global data across sessions, and manage test sessions explicitly.

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