Most research teams have gotten used to a gap in their workflow. On one side, you have a rich Article Galaxy library with thousands of licensed articles, synced subscriptions, shared folders and years of careful choices. On the other, you’re using AI more and more to help with research tasks like finding, summarizing and synthesizing information. But these two tools don’t really connect.
Your AI assistant can point out that a paper exists and might even give you a decent abstract summary. But when it comes time to retrieve the full text you already paid for, check your access rights, or acquire a new article, you have to leave the AI tool and switch to your browser to use a different system. This constant switching is frustrating and slows down every researcher across every project.
That's exactly what Article Galaxy MCP is built to fix.
MCP stands for Model Context Protocol. It is a standard that allows AI tools to connect with external systems and do more than just pull up information. Instead of acting like a browser plugin, it’s more like giving your AI agent a keycard to a building it could only look at from outside before.
With Article Galaxy MCP, AI tools link ChatGPT, Claude, Microsoft Copilot, Cursor, Claude Code, and any MCP-compatible tool can search scientific literature, check your organization’s existing rights, place acquisition orders, and download full-text content, all without leaving the AI environment. The workflow that used to require a series of separate tools now runs in one continuous thread.
If your team already uses Article Galaxy, you can now access your library directly within your AI applications. There’s no need to redesign or move anything. You’re simply connecting your existing setup.
The Article Galaxy MCP can perform seven specific actions, grouped into four main tasks.
The search_literature action lets agents search peer-reviewed papers and access their full text. The get_full_text action provides even more detail by retrieving article content through Smart Citation snippets, organized by sections like Introduction, Methods, Results, and Discussion. The system automatically checks and respects access rights, including open access, subscriptions, and purchased AI rights.
Before showing any content to a researcher, an agent can use check_rights to confirm access status, AI rights pricing, and document delivery options. The check_reuse_rights action also checks if an article can be shared, adapted, or used in other outputs. This is especially important for teams working in regulated or compliance-sensitive settings.
The place_order action handles document delivery (RequestPDF), AI rights only (RequestGenAI), both (RequestPDFAndGenAI), or rental (RequestRental). It requires explicit user confirmation before executing, which keeps human oversight in the loop even when the agent is doing the legwork.
Once an order is placed, check_order monitors its status, whether Sourcing, Completed, or Failed, and get_order_urls retrieves the pre-signed download links for PDFs, supplemental materials, or rental viewer access.
The typical workflow follows a simple sequence: search_literature, get_full_text, check_rights, confirm, place_order, check_order, then get_order_urls. From start to finish, everything happens within your AI environment, and Article Galaxy’s system takes care of all rights and compliance decisions.
Research has always required two things working in parallel: the ability to evaluate what's worth knowing, and the infrastructure to actually get it. For a long time, those were separate problems solved by separate tools. Research Solutions is building the layer that brings them together, a set of modular components designed to support the full workflow of modern, AI-powered research.
Article Galaxy MCP is one of four components in that agentic research suite, alongside the Scite API, Scite MCP, and Article Galaxy API. The Scite layer provides upstream intelligence, such as citation context, paper credibility signals, and claim-level analysis. Article Galaxy manages document access, acquisition, and rights. Together, they cover the full research intelligence stack from "does this finding hold up?" to "here's the licensed full text."
Developers and research engineering teams can mix and match components depending on what they're building. A custom literature review tool might call the Scite API for credibility signals and the Article Galaxy API for retrieval. An agent running inside Claude or Copilot might rely entirely on the MCP layer. The architecture is modular by design.
Article Galaxy MCP is now available. If your organization already has an Article Galaxy library, you can connect easily without needing to migrate data or change your setup. If you're working from an existing literature collection outside Article Galaxy, you can import it to start using MCP.
Agentic research workflows are only as good as the content feeding them. Getting that content layer right, compliantly, efficiently, and easy to access within your AI environment, is what separates a proof of concept from a real research operation.
And most teams aren't short on licensed content to fuel that operation. They're short on a way to put it to work. That’s a much easier problem to solve.