Medium MCP API Server

Medium MCP API Server

The Medium MCP API Server is a dedicated bridge that lets AI assistants and external applications publish, manage, and schedule content directly on Medium. By exposing a Model Context Protocol interface, it abstracts the complexity of Medium’s OAuth flow and REST endpoints into a single, AI‑friendly contract. Developers can therefore issue high‑level commands—such as “create a draft,” “schedule a post for tomorrow,” or “upload an image”—without handling authentication tokens or parsing Medium’s response payloads.

At its core, the server resolves a common pain point for content‑centric AI workflows: seamless integration with a third‑party publishing platform. Many conversational agents need to generate articles, tutorials, or newsletters on the fly, but Medium’s native API requires manual OAuth redirects and careful rate‑limit handling. The MCP server encapsulates these concerns behind JWT‑based authentication, Redis‑backed caching, and a job queue that guarantees posts are published even when the Medium API throttles requests. This reliability is crucial for production AI agents that must maintain a steady publishing cadence.

Key capabilities include:

• User and OAuth management – Securely store Medium credentials, issue JWTs, and expose a single “connect Medium” endpoint that returns the authorization URL.
• Rich content handling – Accept Markdown or HTML, support tags and publication IDs, and expose draft creation, editing, and scheduling.
• Media utilities – Upload images to Medium’s asset store, automatically embed them in posts, and provide formatting helpers.
• Performance safeguards – Redis caching reduces duplicate calls, while a background scheduler handles delayed publishing and respects Medium’s rate limits.

Typical use cases span from automated newsletter generators that pull data from external APIs, to AI‑driven content studios where writers receive instant publishing feedback. For example, a conversational agent can draft an article from user prompts, upload supporting images, and schedule the post for peak engagement times—all through a single MCP call. In enterprise settings, teams can embed the server into their CI/CD pipelines to auto‑publish blog updates whenever a new feature is released.

Because the MCP server standardizes Medium interactions, developers can plug it into any AI workflow that supports Model Context Protocol. Whether the assistant is built on Claude, GPT‑4, or a custom model, it can issue declarative commands, receive structured responses, and handle errors uniformly. This tight integration eliminates boilerplate code, reduces the risk of authentication mishaps, and frees developers to focus on content quality rather than API plumbing.