Overview of ckanext‑mcp

ckanext‑mcp is a Model Context Protocol (MCP) server extension for CKAN that empowers dataset editors to expose precise, question‑answering capabilities for their resources. In traditional CKAN deployments, datasets are discoverable and downloadable but lack a structured interface for AI assistants to ask contextual questions. This extension bridges that gap by allowing editors to define which resources can be queried and how the answers should be generated, turning static data into an interactive knowledge source.

The server transforms CKAN resources into MCP‑compliant endpoints. When a user asks an AI assistant a question about a dataset, the assistant can forward that query to the MCP server. The server then retrieves relevant data from the specified CKAN resources, applies any configured processing rules, and returns a structured response that the assistant can present. This workflow eliminates the need for custom API wrappers or manual data extraction, giving developers a single, standardized channel to tap into CKAN content.

Key features of ckanext‑mcp include:

• Resource selection – Editors can choose which files or data tables are answerable, ensuring sensitive or irrelevant assets remain private.
• Answer shaping – The extension supports simple configuration to format responses, enabling consistent and readable output for AI assistants.
• MCP compliance – By adhering to the Model Context Protocol, the server guarantees interoperability with any AI client that implements MCP, such as Claude or other LLM‑powered assistants.
• Alpha‑ready for experimentation – While still in development, the extension offers a clear path to integrate AI question‑answering into existing CKAN workflows without major architectural changes.

Typical use cases span a range of organizations that rely on CKAN for data publishing:

• Government portals where citizens can ask questions about public spending, demographic statistics, or environmental metrics directly from the dataset pages.
• Research institutions that wish to let collaborators query experimental results or clinical trial data without exposing raw files.
• Open‑data communities that want to provide conversational interfaces for non‑technical users, improving accessibility and engagement.

Integrating ckanext‑mcp into an AI workflow is straightforward: developers add the plugin to CKAN, configure which resources are answerable, and point their AI assistant’s MCP client at the server URL. The assistant can then issue natural‑language queries, receive structured answers, and present them in chat or voice applications. Because the server handles data retrieval and formatting, developers can focus on building richer conversational experiences rather than managing API endpoints.

In summary, ckanext‑mcp turns CKAN into a conversational data hub. It solves the problem of static, discoverable datasets by adding an AI‑ready interface that lets editors expose only the desired information. Its MCP compliance, ease of integration, and focus on developer control make it a compelling tool for any organization looking to unlock the conversational potential of their CKAN data.