Discourse MCP Server

Overview

The Ashdev Discourse MCP Server provides a lightweight, Node.js‑based bridge between AI assistants—such as Claude—and the rich discussion data hosted on a Discourse forum. By exposing a single, well‑defined tool () through the Model Context Protocol (MCP), developers can give their AI agents the ability to query forum content without exposing raw API credentials or writing custom integration code. This solves a common pain point: enabling conversational agents to surface relevant community knowledge in real time while keeping security and scalability concerns under tight control.

The server’s core functionality is to perform keyword searches against a Discourse instance and return structured post objects. The tool accepts a simple string, forwards it to the Discourse REST API using the configured URL, key, and username, and then normalizes the response into an array of post metadata (e.g., title, author, excerpt). Because the MCP server runs as a separate process, it can be orchestrated with Docker or NPX, allowing developers to spin up isolated instances per project or environment. The configuration is straightforward: just supply the API endpoint, key, and username as environment variables or Docker arguments, and the server will handle authentication and request routing automatically.

Key capabilities include:

• Secure API delegation – credentials never leave the server process; the AI client only interacts with the MCP interface.
• Consistent response format – all search results are returned as a predictable array of objects, simplifying downstream processing in the assistant’s prompt.
• Scalable deployment – Docker or NPX execution makes it easy to run multiple instances behind a load balancer if needed.
• Extensibility – the toolset can be expanded to support additional Discourse endpoints (e.g., user lookup, topic listing) without changing the MCP contract.

Typical use cases span from customer support bots that need to surface community solutions, to knowledge‑base assistants that pull recent discussions into product documentation, or even moderation tools that surface potentially problematic posts for review. In each scenario, the MCP server acts as a thin middleware layer that abstracts away API intricacies while preserving performance and security.

Integrating this server into an AI workflow is seamless: add the MCP configuration to your , start the server, and then invoke the tool from within your assistant’s prompt. The MCP client will automatically send the query, receive the structured array, and allow the model to incorporate the retrieved posts into its response. This tight coupling enables richer, context‑aware interactions without burdening developers with repetitive API plumbing.