"Tools for Testing in Python and Java" matching MCP servers:

Bridges agentic coding tools and live Java runtime behavior through a lightweight sidecar agent. Attaches directly to a running JVM to provide bytecode-level runtime signals for probe-verified inspection and deterministic debugging.

Decompiles Maven dependencies into readable Java source directly inside your AI agent.

A Model Context Protocol (MCP) server that provides testing and debugging tools for Stripe integrations, including test clock management, customer and product management, and subscription testing.

Automates Java Maven testing workflows with decision table-based test generation, security vulnerability scanning, JaCoCo coverage analysis, and Git automation.

Provides Java development capabilities through Eclipse JDT.LS, enabling symbol navigation, code diagnostics, workspace searching, and Javadoc access across Java projects.

Enables to interact with Tsurugi RDBMS by executing SQL (DDL, DML, SELECT) and retrieving metadata through MCP.

Enables automatic generation of explicit boilerplate methods (getter/setter/equals/hashCode/toString/constructors) for Java entity classes, replacing Lombok dependencies with visible, reviewable code.

Enables AI to analyze Java classes from Maven project dependencies, with support for decompilation strategies and configurable groupId mappings.

Enables AI coding agents to look up live JDK javadoc, Spring Boot reference sections, and Maven Central artifact metadata as clean markdown. It provides ranked search, caching, robots-aware throttling, and errors returned as suggestions instead of raising into the MCP layer.

MCP server for testing GitHub Actions workflows locally using nektos/act.

A lightweight, local-first MCP server for executing HTTP requests and managing API collections and environments without cloud dependencies. It enables testing APIs, handling authentication, and importing OpenAPI specifications directly within MCP-compatible workflows.

An MCP server that uses the CFR decompiler to convert Java .class and .jar files back into readable source code. It supports single-file, batch, and recursive directory decompilation with automated CFR tool management.

A high-performance MCP server that bridges AI agents with Java codebases, providing professional-grade Java language intelligence via Eclipse JDT.LS.

A Codex MCP server that provides low-token Java semantic navigation using source indexing and optionally JDT Language Server for enhanced symbol, references, and diagnostics.

A comprehensive MCP toolkit for Java backend developers, providing 35 tools across 5 servers for database analysis, JVM diagnostics, migration assistance, Spring Boot monitoring, and Redis diagnostics.

Enables AI assistants to look up Java class definitions and list dependencies from Maven projects by analyzing local JAR files via the Model Context Protocol.

A Java implementation of the GitHub MCP (Model Context Protocol) Server using Spring AI. This server provides MCP-compatible tools for interacting with the GitHub API.

Automates Java/Maven test generation, JaCoCo coverage analysis, and Checkstyle checking through MCP tools. Enables iterative improvement of test coverage and code style until quality goals are met.

Enables automated testing and coverage reporting for MCP services with test execution, file generation, and mock service creation. Provides comprehensive testing infrastructure including Jest integration, coverage reports, and health checks for the MCP ecosystem.

MCP server that enables AI agents to programmatically run tests, query results, and receive intelligent recommendations about test execution strategy.

Exposes 120+ Chinese frontier AI models (DeepSeek, Qwen, GLM, Kimi, Seedance, MiniMax, Hunyuan, CogVideoX) as MCP tools via one OpenAI-compatible endpoint: chat, image generation, free-model list, and a live first-party price leaderboard. Zero platform fee.

This MCP server provides direct access to ruff linting, formatting checks, and ty type-checking for Python projects, with token-efficient, structured output.

Enables building, starting, stopping, restarting, inspecting, and monitoring registered Spring Boot + Maven projects on macOS, including status checks and log tailing through MCP tools.

Enables AI tools to analyze Java dependencies by scanning Maven projects, decompiling JAR files, and extracting detailed class information including methods, fields, and inheritance relationships. Solves the problem of AI hallucinations when generating code that calls external dependencies by providing accurate class structures through decompilation.

A Model Context Protocol server that provides Azure Java SDK documentation to AI assistants, allowing them to access readme files with introductions, key concepts, and code samples.

Enables academic research through paper search across multiple databases (IACR, CryptoBib, Crossref, Google Scholar), PDF processing, and GitHub repository browsing. Features modular architecture with FastMCP-based proxy server routing to specialized academic tools.

Enables AI agents to inspect UI screen recordings as token-efficient image sheets with changed regions boxed, single-frame flicker flagged, and speech aligned to frames. Exposes watch_video and zoom_video tools so agents can pull dense keyframes across any time range or one sharp full-resolution frame, all processed locally.

A multi-language testing automation server that enables AI assistants to discover, plan, and execute tests across six major programming languages. It provides comprehensive tools for repository scanning, automated test execution, and detailed coverage reporting.

Enables any MCP client to drive the user's real Chrome profile through a Chrome extension bridge, with tools for navigation, accessibility snapshots, ref-based interactions, console/network reading, screenshots, tab management, and restricted script evaluation.

Enables agents to review Python dependency requirements and source code against a modernization catalog, returning structured guidance, import-level suggestions, and compatibility floors. It also lists available codemod recipes and previews their transformations as unified diffs, all read-only and without installing packages or executing supplied code.

A local MCP server that enables AI applications like Claude Desktop to securely access and work with Obsidian vaults, providing capabilities for reading notes, executing templates, and performing semantic searches.

Search and pull 620+ React and Tailwind sections into your project over MCP.

Enables users to describe an LLM fine-tuning job and receive ranked, task-aware GPU recommendations with estimated VRAM/FLOPs, cost, and runtime across more than ten cloud providers.

Enables users to describe an LLM fine-tuning job and receive the cheapest, fastest, or most balanced GPU options across multiple cloud providers by calculating required VRAM and FLOPs.

Enables users to describe an LLM fine-tuning job and receive ranked GPU recommendations across a dozen cloud providers based on estimated VRAM/FLOPs, live pricing, cost, and speed. It returns cheapest, fastest, and balanced hardware options without manual comparison.

A demonstration server showing MCP implementation in Python with resource handling, tool operations, and reusable prompts for a simple user/post system with local database.

Enables users to describe an LLM fine-tuning job once and get ranked GPU hardware recommendations with estimated VRAM/FLOPs, cost, and runtime across a dozen cloud providers.

It enables users to describe an LLM fine-tuning workload once and automatically estimates required VRAM/FLOPs, compares live GPU pricing across cloud providers, and returns ranked cheapest, fastest, or balanced hardware recommendations.

Enables users to describe an LLM fine-tuning workload and receive ranked, task-aware GPU recommendations by estimating required VRAM/FLOPs and comparing live pricing across a dozen cloud providers.

Enables users to describe an LLM fine-tuning job once and receive ranked GPU recommendations across many cloud providers based on estimated VRAM/FLOPs, cost, and runtime.