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Investigate customer errors and draft tickets in support

Cursor's support team uses Cursor with MCP to pull Datadog logs, search past cases, and draft a Linear bug ticket and customer reply.

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Use case overview

Cursor's support team uses Cursor with MCP to pull Datadog logs, search past cases, and draft a Linear bug ticket and customer reply.

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Reported experience

  1. What they did

    Support engineers start in Ask Mode with the codebase in a multi-root workspace. MCP servers bring in Datadog logs and traces, Slack and Notion history, and Linear. Slash commands and skills cover log search, known-issue lookup, ticket drafting and customer replies. Parallel subagents (log investigator, known-issue miner, ticket writer, reply drafter) merge into one output that a person reviews and sends.

  2. How it went

    Over 75% of support interactions now run through Cursor, and the team estimates 5–10x throughput per support engineer. These figures are the team's own estimates, not measured results.

  3. Worth knowing

    Write guardrails into the reply prompts so drafts leave out internal service names, error codes and file paths. A human still reviews everything before it goes out.

Example prompts

Try with Cursor
A customer reported [error description] on [date/account]. Using my connected [Datadog / logging tool], [past case search], and [Linear / Jira], find the relevant logs and similar past cases, then draft a bug ticket and a customer reply. Show me both drafts with the evidence you found, and do not file the ticket or send the reply until I approve.

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