MediaWiki MCP Adapter

The MediaWiki MCP adapter bridges AI assistants with the powerful ecosystem of MediaWiki and WikiBase. By exposing a minimal set of resources and tools over the Model Context Protocol, it lets developers treat Wikipedia‑style wikis as first‑class data sources or editing targets for their AI workflows. This solves the common pain point of having to write custom HTTP clients, handle authentication, and parse MediaWiki’s JSON responses—tasks that are repetitive across many projects.

At its core the server offers two straightforward capabilities: and . The former retrieves the raw wikitext of any page given its title, while the latter submits a new revision with optional edit summaries. Both endpoints are defined by clear JSON schemas, ensuring that AI assistants can validate inputs and outputs before invoking the service. Because the adapter is built on MCP, these operations are discoverable at runtime and can be composed with other tools in a larger workflow.

Key features include:

• Configurable endpoints: Swap between public wikis (e.g., Wikipedia, Wikidata) and private instances by overriding the API base URLs at startup.
• Rich editing support: The edit tool accepts a full wikitext payload and an optional summary, allowing AI agents to generate or refine content in the same format as human editors.
• Security‑ready: While authentication is not hard‑coded, the adapter can be extended to include API tokens or OAuth flows, making it suitable for both read‑only bots and trusted editors.

Typical use cases are abundant. A knowledge‑base assistant can pull the latest article text, analyze it for sentiment or factual consistency, and then push a corrected version back to the wiki. A data‑engineering pipeline might extract structured information from pages, enrich it with external datasets, and update the wiki to reflect new insights. In research settings, an AI could draft summaries of long articles and submit them as edit suggestions for human reviewers.

Integrating the adapter into an MCP‑enabled AI stack is seamless: once the server is running, any client that understands MCP can discover the and resources, send requests with simple JSON payloads, and receive typed responses. This removes boilerplate, reduces the surface area for bugs, and allows developers to focus on higher‑level logic rather than API quirks. The adapter’s lightweight design and clear documentation make it an attractive choice for teams that need reliable, programmatic access to MediaWiki data within their AI workflows.