Cursor Memory MCP

The Cursor Memory MCP is a specialized bridge that lets AI assistants such as Claude seamlessly create, update, and query project‑specific memory files inside Cursor’s directory. By exposing a single, well‑defined MCP interface, it eliminates the need for custom scripts or manual file handling, allowing developers to focus on higher‑level logic while the server guarantees that every task’s state is captured in a file that follows Cursor’s strict schema.

At its core, the server solves the perennial problem of persistent context for AI‑augmented development. When an assistant executes a series of steps—code generation, testing, refactoring—the resulting context (inputs, outputs, decisions) can be serialized into a memory record. Subsequent invocations of the assistant automatically reference these records, ensuring continuity across sessions and preventing duplicate work. This is especially valuable in large codebases where multiple assistants or team members may act on the same repository; a shared memory store keeps everyone in sync without manual intervention.

Key capabilities include:

• Automatic memory record creation: After each task, the server writes a file that captures the full execution context, including prompts, responses, and relevant file changes.
• Schema‑aware formatting: Files are generated according to Cursor’s specification, guaranteeing compatibility with the editor and rule engine.
• Cross‑platform operation: The MCP client works uniformly on Windows, macOS, and Linux, making it suitable for diverse development environments.
• Multilingual support: Both the server and its output are fully internationalized, accommodating Chinese and other languages without loss of fidelity.
• Standard MCP compliance: By adhering to the Model Context Protocol, it integrates natively with any MCP‑enabled AI assistant, requiring only a minimal configuration change.

Typical use cases include automated refactoring pipelines where an assistant iteratively improves code while preserving a history of changes, or documentation generation that references prior design decisions stored in memory files. In continuous integration workflows, the server can capture test results and bug reports, enabling future assistants to propose targeted fixes based on historical patterns.

Integrating the Cursor Memory MCP into an AI workflow is straightforward: developers add a single server entry to Cursor’s MCP settings, and the assistant automatically gains access to the tool. From there, it can instruct the server to write or read memory files as part of its natural language prompts. This tight coupling means that AI agents can treat project memory as first‑class data, just like code or configuration files, leading to more coherent and contextually aware interactions.