CircleCI MCP Server

The CircleCI MCP Server bridges the gap between large language models and continuous‑integration workflows. By exposing a set of well‑defined tools over the Model Context Protocol, it lets AI assistants—such as Cursor or any MCP‑compatible client—talk directly to CircleCI’s REST API. This eliminates the need for manual browsing of dashboards or writing custom scripts, enabling developers to retrieve build information, debug logs, and pipeline status through natural‑language queries.

At its core, the server implements a single powerful tool: . The tool accepts either an explicit CircleCI URL (pipeline or job) or a local project context—root path, git remote, and branch name. In the latter case, the server automatically discovers the most recent failed pipeline on that branch and streams back a richly formatted log dump. The output includes job names, step execution details, failure messages, and contextual notes, all formatted for quick consumption inside an IDE. This capability is invaluable when debugging flaky tests, diagnosing deployment errors, or simply tracking down why a recent push broke the build without leaving the editor.

For developers working with AI assistants, this integration offers several tangible benefits. First, it reduces context switching: a single prompt can trigger the assistant to fetch and display logs, allowing developers to focus on code rather than navigating web interfaces. Second, the server’s declarative approach ensures consistent data retrieval across projects; the same tool works for any repository with a valid CircleCI token. Third, by exposing failure logs directly to the assistant, teams can embed automated triage or remediation suggestions—e.g., “Run tests again” or “Open a PR to fix the failing step”—right within their workflow.

Typical use cases include continuous‑integration monitoring, rapid incident response, and automated pipeline analytics. A developer might say, “Show me the latest failure on my feature branch,” and receive a parsed log with highlighted error messages. QA engineers can ask, “What tests failed in the last build?” and get a concise summary, while DevOps engineers can integrate the tool into chat‑ops or CI dashboards for real‑time insights. Because the server leverages CircleCI’s API tokens and respects standard authentication flows, it fits neatly into existing security models.

In summary, the CircleCI MCP Server transforms CI data from a passive dashboard into an active conversational partner. By exposing a single, highly useful tool over MCP, it empowers AI assistants to streamline debugging, accelerate feedback loops, and keep developers focused on writing code rather than chasing logs.