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    Official MCP server for Qase Test Management Platform. Enables AI assistants to manage test cases, test runs, defects, and other Qase entities through natural language.
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    MIT
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    Enables LLM agents to programmatically inspect, debug, and test other MCP servers by wrapping the MCP Inspector CLI. Supports listing and calling tools, reading resources, and testing prompts on both local and remote MCP servers.
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    MIT
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    Enables real-time code analysis for JavaScript, TypeScript, and Python through Claude Desktop and other MCP clients, detecting bugs, code smells, and security vulnerabilities with automated quick fixes.
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    An MCP server that gives Claude and other AI agents the ability to audit any public URL's HTTP security headers. What it checks: * HSTS, CSP, X-Frame-Options, X-Content-Type-Options, Referrer-Policy, Permissions-Policy * HTTPS enforcement and redirect chain depth * Presence of security.txt, robots.txt, sitemap.xml Payment model: * 0.05 USDC per scan, paid automatically on Base via the x402 proto
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    MIT
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    Integrates MantisBT bug tracker into Claude and other MCP clients via the REST API. Read and manage issues, notes, file attachments, tags, relationships, and monitors — with optional offline semantic search across all issues.
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    An MCP server that enables coding agents to autonomously test, evaluate, and tune other MCP servers by acting as a proxy and providing linting, trace recording, evaluation, comparison, and reporting tools.
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    MIT
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    Connects AI coding assistants to TestCollab for managing test cases, plans, and suites directly through natural language. It enables users to create, update, and query testing resources within integrated development environments and AI chat clients.
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    Exposes the Maester security test framework for Microsoft 365/Entra/Defender as MCP tools, enabling AI agents to run tests, audit AI agents and MCP servers, and investigate security posture.
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    MIT
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    AI-native Model Context Protocol (MCP) server for TestRail. Lets Claude, Cursor, Windsurf, and other AI assistants browse projects, create and update test cases, kick off test runs, and record results through natural-language conversation — with strongly-typed tool schemas and per-project custom field validation that helps LLMs generate valid TestRail requests on the first try.
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    Apache 2.0
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    Enables AI agents to programmatically inspect, test, and validate other MCP servers by exposing MCP Workbench capabilities as structured tools. It supports automated test spec generation, execution, and detailed failure analysis to ensure server reliability.
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    A specialized testing harness that enables AI assistants to thoroughly test other MCP servers by connecting to them, discovering their tools/resources/prompts, executing test calls, and performing end-to-end validation with LLM integration.
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    Enables AI assistants to interact with Coval's evaluation platform for launching and monitoring evaluation runs, managing agents and test sets, and retrieving evaluation metrics.
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    Enables interaction with AWS Fault Injection Service to create, manage, and execute chaos engineering experiments. Operates in read-only mode by default for security, with optional write operations for starting/stopping experiments and managing templates.
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