BlazeMeter MCP server

MCP server is a modular, extensible service that exposes tools, data, and workflows to AI models via the Model Context Protocol (MCP). It acts as a bridge between AI agents and real-world systems, enabling seamless interaction with APIs, databases, applications, and more.

The BlazeMeter MCP server connects AI tools directly to BlazeMeter's cloud-based performance testing platform. This gives AI agents, assistants, and chatbots the ability to manage complete load testing workflows from creation to execution and reporting. All through natural language interactions.

BlazeMeter MCP server is available for BlazeMeter Performance testing and Service Virtualization.

Use cases

The BlazeMeter MCP server is built for developers and QA teams who want to connect their AI tools to BlazeMeter's enterprise-grade performance testing capabilities, from simple test creation to complex multi-step automation workflows.

  • Performance Test Management: Create, configure, and manage performance tests with automated script uploads and asset management.

  • Test Execution & Monitoring: Start tests, monitor execution status, and retrieve comprehensive reports including summary, errors, and request statistics.

  • Workspace & Project Organization: Navigate through accounts, workspaces, and projects to organize your testing infrastructure.

  • Load Configuration: Configure test parameters including concurrency, iterations, duration, ramp-up settings, and geographic distribution.

  • Report Analysis: Access detailed execution reports, error analysis, and performance metrics for comprehensive test insights.

  • Account & Permission Management: Manage multiple accounts and workspaces with proper AI consent controls and permission validation.

  • Service Virtualization: Create and run virtual services that emulate the behavior of unavailable web services. To learn more, see Service virtualization MCP server.

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Install BlazeMeter MCP Server

For details on installation and configuration, see the GitHub README documentation.

You can deploy BlazeMeter MCP Server locally.

Installation options:

  • Installation using our interactive CLI tool

  • Manual client configuration

    • Binary installation

    • from remote source code

  • Docker MCP Client Configuration

Prerequisites:

  • BlazeMeter API credentials: API Key ID and Secret.

  • Compatible MCP host (VS Code, Claude Desktop, Cursor, Windsurf, etc.)

  • (Only for Docker-based deployment:) Docker

  • (Only for installation from source code distribution:) uv (a high-performance Python package) and Python 3.11+.

  • BlazeMeter's AI-assisted features require account owner consent. For more information, see Manage AI Consent.