Medium MCP Server

The Medium MCP Server is a specialized bridge that lets AI assistants, such as Claude, tap directly into Medium’s vast library of articles and author content. By exposing a Model Context Protocol (MCP) interface, the server transforms Medium’s RESTful endpoints into declarative resources that can be queried, filtered, and analyzed by conversational agents. This eliminates the need for developers to write custom HTTP clients or handle authentication flows manually, enabling seamless integration of Medium’s data into AI‑driven workflows.

For developers building content discovery or recommendation systems, the server solves a two‑fold problem: first, it abstracts away Medium’s complex API mechanics; second, it enriches raw data with AI‑powered insights. The server exposes intelligent content querying capabilities—agents can request articles by topic, author, publication date, or engagement metrics—and receives structured responses that include metadata, full text, and contextual tags. This allows applications to surface relevant Medium posts in real time or feed them into downstream natural‑language processing pipelines.

Key features are designed with practicality and extensibility in mind. The server offers intelligent content extraction, where the raw HTML of a Medium article is parsed into clean, machine‑readable text while preserving formatting cues. It also provides context‑aware analysis, enabling agents to ask follow‑up questions about an article’s sentiment, key themes, or author tone without additional code. The MCP interface includes resource definitions for authors, publications, and tags, making it straightforward to construct compound queries that combine multiple criteria.

Real‑world use cases span content curation, market research, and educational tools. A news aggregator can let an AI assistant pull the latest Medium pieces on emerging technologies, automatically summarizing them for readers. A marketing team might use the server to discover thought leaders in their niche, then generate outreach messages tailored to each author’s style. Educators can build study aids that pull Medium tutorials and annotate them with pedagogical notes, all orchestrated through conversational commands.

Integration into existing AI workflows is effortless. Once the server is running, any MCP‑compatible client can reference its resources by name; the AI assistant then invokes these resources as part of a dialogue, receiving structured JSON payloads that can be rendered or further processed. Because the server is built in TypeScript and follows MCP best practices, developers can confidently extend its capabilities—adding new parsing rules or custom metrics—without breaking the contract that AI assistants rely on. This tight coupling between Medium’s content ecosystem and conversational agents unlocks powerful, context‑rich interactions that would otherwise require significant engineering effort.