MCP Server v0.12.0 is live — what’s new and why it matters
Hello MCP community, We just pushed the v0.12.0 release of SmartBear MCP Server — a solid step forward in adding new capabilities across our ecosystem of products. What’s new in v0.12.0 Zephyr support improvements — we added two new tools: one to retrieve Environments, and another to fetch a list of Test Executions. Swagger enhancements — two powerful tools added: create_api_from_prompt: generate API definitions directly from natural-language descriptions, powered by SmartBear AI, with governance and standardization applied. standardize_api: scan an existing API definition and automatically fix it to comply with standardization rules — helpful for teams managing many APIs or enforcing policies. BugSnag observability extension — added tools for querying performance data and managing network-grouping rules: list/get span groups, list spans, get traces, list trace fields, and get/set network endpoint groupings. QMetry automation & context metadata improvements — new tools for importing automation results, creating releases/cycles, and improved contextual metadata to help with LLM-based prompt handling. Misc tools & UX improvements — support for a --version command line flag to easily check which MCP version you’re running. Breaking/Changed Behavior in BugSnag integration — event threads have been removed from the “Get Error” response; instead, there is now “Get Event (by ID)”. If your automation or scripts rely on the old shape, you’ll need to update. Why this matters Teams using Zephyr now have deeper visibility into environments and test executions directly from MCP clients. That’s a big win for QA workflows and auditability. For API-first teams, the new Swagger AI-powered API creation and standardization tools could drastically speed up API lifecycle: from “idea” to “compliant spec” in one step — helping enforce consistency and governance at scale. The expanded BugSnag performance/tracing support bridges the gap between observability and AI-driven diagnostics. You can now query traces, spans, and network groupings — which makes MCP a more compelling tool for real-time debugging and error triage. Improved QMetry automation import + LLM context metadata makes it more practical to embed test results and context into AI-powered workflows — good for release automation, reporting, or even automated triage. The release overall tightens integration across the SmartBear ecosystem — pushing farther toward a unified, AI-ready platform. What to do next Upgrade to v0.12.0. If you use BugSnag integrations — especially error queries — verify your scripts against the changed “Get Error / Get Event” shape. Try out the new Swagger and Zephyr tools. Think about how these could reshape your API and QA workflows. If you’re experimenting with AI-driven workflows (e.g. auto-generating APIs or linking runtime errors to API docs) now is a good time to test and give feedback. As always: share your experiences. What works? What needs polish? Your feedback will guide what’s next. This is a big release with real substance behind it. For teams already invested in BugSnag, Swagger, Zephyr or QMetry, adopting v0.12.0 could unlock real productivity and automation opportunities.