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    An MCP server that emits OpenTelemetry traces, metrics, and logs to OTLP endpoints in various formats. It includes tool-mimicry profiles to generate realistic traffic resembling common tools like nginx, postgres, and AWS Lambda for testing and demonstration purposes.
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    11
    MIT
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    Enables the execution and analysis of JMeter performance tests through MCP-compatible clients. It provides tools for running tests in non-GUI mode, identifying performance bottlenecks, and generating comprehensive insights and visualizations from result files.
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    Enables QA/SDET engineers to test APIs by ingesting Swagger/OpenAPI specs and Postman collections, generating and executing tests in multiple languages and frameworks with real-time progress tracking.
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    MIT
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    Enables AI assistants to interact with SmartBear's suite of testing and monitoring tools including BugSnag, Swagger, Reflect, and more through natural language, allowing querying of testing data, analysis of performance metrics, and management of test automation.
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    MIT
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    Allows AI assistants such as Cursor/Cline/GitHub Copilot to use Google's lighthouse tool to measure perf metrics for your webpage. You can then run an agentic loop and get the assistants to optimize those metrics!
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    MIT
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    Enables comprehensive analysis of JavaScript/TypeScript project testing setups by detecting frameworks like Jest, Vitest, and Cypress, analyzing test coverage metrics, and generating actionable recommendations for improving test quality. Provides detailed insights into test structure, dependencies, and coverage thresholds with visual feedback.
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    Enables comprehensive GitHub PR reviews through Cursor's AI by fetching PR diffs, running static analysis tools (ESLint, Prettier, TypeScript, Semgrep), executing tests, and generating detailed code review reports with inline comments.
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    Provides advanced evaluation tools for assessing AI safety, alignment, and performance of LLM outputs. Enables programmatic evaluation of quality, safety metrics like toxicity and PII detection, and operational metrics including carbon footprint and cost estimation.
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    Apache 2.0
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    A multi-task MCP server for AI code optimization and testing that integrates with VSCode/Cursor, CLI tools, and autonomous agents. It provides tools for running tests, generating code improvements, and managing objectives across different environments.
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    Apache 2.0
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    Provides unified development tools including code analysis, debugging, refactoring, documentation, testing, and project automation through multiple LLM providers (KIMI, GLM, OpenRouter). Features agentic audit capabilities with multi-model consensus for finding issues and generating direct fixes.
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    A frontend security dependency auditing tool that identifies vulnerabilities in local and remote repositories using the Model Context Protocol. It provides detailed audit information like CVSS scores and dependency chains, generating standardized markdown reports.
    Last updated
    7
    MIT