Use LogRocket
When to use
- Debugging a user-reported issue or error
- Understanding how a feature is currently being used before making changes
- Triaging new issues and figuring out root causes
- Checking for regressions after recent deploys or commits
- Prioritizing what to work on next based on real user impact
- Investigating JavaScript errors, rage clicks, or dead clicks
- Analyzing post-launch behavior for a new feature
- Researching a specific user or account's sessions
Instructions
- Call the
use_logrocket MCP tool with a natural language query describing what you want to know.
- If the user hasn't specified an organization or project, use the
list_organizations and list_projects tools to discover them. If multiple are found, ask the user which to use.
- To continue the same conversation (e.g. follow-up questions, drilling deeper), pass the
chatID from the previous response.
- Be specific about what you want analyzed — mention URLs, click targets, user emails, time ranges, or custom events when possible.
- Ask LogRocket to watch sessions when you need detailed, qualitative insights about user behavior.
- Present results clearly to the user, including any session URLs, metrics, charts, or actionable insights.
Example Prompts
- Debug user-reported issues: "User X reported a problem with checkout. Can you use LogRocket to watch their sessions and figure out the root cause?"
- Understand feature usage: "I'm about to work on the search feature — can you use LogRocket to help me understand how it's currently being used?"
- Triage new issues: "Can you look at LogRocket for new issues from the past week, try to figure out their root causes, and then suggest which ones I can fix?"
- Check for regressions: "Look at all commits from last week, and check LogRocket data to ensure they didn't introduce any regressions."
- Prioritize your work: "Use LogRocket to watch sessions and look at issues to figure out what is highest priority that I work on next."
Suggested Automations
Because the MCP server can be called programmatically by AI agents, you can set up powerful automations that continuously leverage LogRocket data:
- Research churning customers and low NPS scores: Automatically pull LogRocket sessions for users who are churning or leaving low NPS scores to understand what went wrong in their experience.
- Research new support tickets: Connect to your help desk to automatically research incoming support tickets using LogRocket session data. LogRocket offers out-of-the-box integrations with Zendesk and Intercom to attach session replays directly to tickets.
- Summarize user behavior for sales and customer success: Automatically generate summaries of how key accounts are using your product, giving your sales and customer success teams actionable insights.
- Connect LogRocket with your backend data: Build a skill that allows your agent to correlate LogRocket frontend data with backend observability tools (e.g. Datadog MCP) for end-to-end debugging.
- Run daily or weekly reports: Schedule an agent to look for new issues and UX frustration signals on a recurring basis, so your team is always aware of emerging problems.