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Merge pull request #14418 from julianbei/add-jade
Add Jade (julianbei/jade) to Developer Tools 🤖🤖🤖
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@@ -1511,6 +1511,7 @@ Tools and integrations that enhance the development workflow and environment man
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- [joshuarileydev/simulator-mcp-server](https://github.com/JoshuaRileyDev/simulator-mcp-server) 📇 🏠 - An MCP server to control iOS Simulators
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- [Jpisnice/shadcn-ui-mcp-server](https://github.com/Jpisnice/shadcn-ui-mcp-server) 📇 🏠 - MCP server that gives AI assistants seamless access to shadcn/ui v4 components, blocks, demos, and metadata.
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- [jsdelivr/globalping-mcp-server](https://github.com/jsdelivr/globalping-mcp-server) 🎖️ 📇 ☁️ - The Globalping MCP server provides users and LLMs access to run network tools like ping, traceroute, mtr, HTTP and DNS resolve from thousands of locations around the world.
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- [julianbei/jade](https://github.com/julianbei/jade) [](https://glama.ai/mcp/servers/julianbei/jade) 🏎️ 🏠 🍎 🐧 - The IDE for agents: read code by symbol, edit against a known revision with compiler diagnostics in the response, validate with the project's own build and tests, and revert to a checkpoint. Tree-sitter plus real language servers for Go, Java, Scala, TypeScript, Python, Rust and Ruby. Official MCP Registry: `io.github.julianbei/jade`. `curl -fsSL https://raw.githubusercontent.com/julianbei/jade/main/install.sh | sh`
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- [Jungle-Grid/mcp-server](https://github.com/Jungle-Grid/mcp-server) [](https://glama.ai/mcp/servers/Jungle-Grid/mcp-server) 🎖️ 📇 ☁️ - MCP server for Jungle Grid, an agentic GPU execution layer that lets AI agents estimate, submit, monitor, and fetch logs for inference, training, fine-tuning, and batch workloads.
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- [kadykov/mcp-openapi-schema-explorer](https://github.com/kadykov/mcp-openapi-schema-explorer) 📇 ☁️ 🏠 - Token-efficient access to OpenAPI/Swagger specs via MCP Resources.
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- [kao273183/mk-qa-master](https://github.com/kao273183/mk-qa-master) [](https://glama.ai/mcp/servers/kao273183/mk-qa-master) 🐍 🏠 🍎 🪟 🐧 - AI 測試大師 — end-to-end QA loop over MCP. Drives pytest / Jest / Cypress / Go / Maestro from one surface; analyzes URLs (Web DOM) or live mobile screens (`maestro hierarchy`) to extract testable modules, generates runnable pytest or Maestro YAML with real selectors (not `# TODO` stubs), runs with auto-retry, then writes a prioritized `optimization-plan.md` ranked by evidence (flaky vs broken vs coverage gap). Mobile-first: iOS Simulator, Android Emulator, real devices, and BlueStacks (`QA_ANDROID_HOST=127.0.0.1:5555`). Install via `uvx mk-qa-master`.
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