LimeSurvey MCP Server

The LimeSurvey MCP Server bridges the gap between conversational AI assistants and LimeSurvey, a widely used open‑source survey platform. By exposing an MCP interface, the server lets AI agents create, update, and retrieve surveys and responses without needing to write custom API wrappers. This is especially valuable for developers who want to automate survey workflows or incorporate real‑time data collection into chat‑based applications.

At its core, the server translates MCP commands into LimeSurvey’s XML‑RPC API calls. When an AI client sends a request—such as “list all active surveys” or “record a response for survey X”—the server forwards the call to the configured LimeSurvey instance, handles authentication via username and password, and returns a structured JSON payload. This seamless translation removes the need for developers to manage authentication tokens or XML parsing, allowing them to focus on higher‑level logic in their AI applications.

Key features include:

• Survey Management: Create, modify, and delete surveys, questions, and question groups through simple MCP actions.
• Response Handling: Submit new responses, fetch existing ones, and aggregate results directly from the AI.
• Configuration Flexibility: Environment variables (, , ) let you point the server at any LimeSurvey deployment, whether on-premise or hosted.
• Secure Integration: The server is accompanied by a security assessment badge, giving developers confidence that the implementation follows best practices.

Real‑world use cases are plentiful. A customer support chatbot could automatically generate a satisfaction survey after an interaction, then analyze responses to trigger follow‑up actions. A research assistant might use the server to collect participant data in real time, feeding results into a natural‑language summary tool. In educational settings, an AI tutor could deploy quizzes via LimeSurvey and adapt subsequent lessons based on student performance.

Integration into existing MCP workflows is straightforward: developers add the server to their configuration, point the command path to the executable, and supply the required environment variables. Once running, any MCP‑compatible client—Claude, GPT-4, or custom agents—can issue survey commands as if they were native tool calls. This tight coupling empowers developers to build end‑to‑end solutions that combine conversational AI with robust survey infrastructure, all while keeping the codebase clean and maintainable.