If you evaluate LLM output with DeepEval, your eval results are some of the most valuable test data you have — and until now they lived in a separate tool from the rest of your tests. testrelic-deepeval changes that: it captures your DeepEval runs and uploads them to your TestRelic org, so evals sit next to your browser, API, and mobile tests with the same history, AI Insights, and dashboards.
The TestRelic MCP server started as a way to give AI coding assistants real testing context — projects, coverage, healing, impact analysis. With 3.1, it grows into a full AI-workflow surface: five new capability sets, a one-click Cursor Marketplace install, and a cleaner tool namespace. It's published as @testrelic/mcp v3.1.2.
Last month we brought Ask AI into Slack. Today we're doing the same for Microsoft Teams — with full parity. Run an Ask AI prompt in a channel, schedule a weekly QA digest, share a dashboard back from the web app, and get a ping the moment a quality gate fails. And alongside it, we're shipping public and private share links for any AI artifact.
Today we're launching the TestRelic Slack App — a way to bring Ask AI, scheduled QA digests, and dashboard share-back directly into the channels where your team already works. Type /testrelic in any channel to ask anything, schedule a weekly flaky digest to #qa-leads, or push any artifact from the web app to Slack in one click.
Ask AI is TestRelic’s primary full-page conversational interface for turning natural language into answers about your organization’s tests, runs, and quality signals—and, when appropriate, into structured artifacts you can share or iterate on. The authoritative specification lives in the Ask AI documentation; this article expands that material with additional cross-links for teams evaluating Ask AI against IDE-only tooling such as the TestRelic MCP server.
Coding assistants are only as good as the context they see. Pasting stack traces into chat scales poorly; re-fetching structured run history from your org is better. The TestRelic MCP server implements the Model Context Protocol so compatible hosts—Cursor, Claude Desktop, VS Code MCP clients, and others—can call tools that read your TestRelic cloud projects, runs, coverage signals, and related data. The MCP overview positions MCP alongside the SDK reporters: reporters write run data from CI and laptops; MCP reads and acts on that data inside the IDE loop.