OpenAlex Author Disambiguation MCP Server

The OpenAlex Author Disambiguation MCP Server is a purpose‑built bridge between AI assistants and the OpenAlex.org scholarly data ecosystem. By exposing a concise set of tools that perform author name resolution, affiliation tracking, and publication retrieval, it solves the persistent problem of author ambiguity—the difficulty of distinguishing between individuals who share similar names or who have changed institutional ties over the course of a career. For developers building research assistants, this means that their agents can query for an author’s complete scholarly footprint without wrestling with noisy raw API responses or manual cross‑matching.

At its core, the server offers a suite of advanced disambiguation capabilities. It ingests name variants and institutional histories, then returns a ranked list of potential matches along with confidence scores. When an ORCID identifier is available, the server leverages it to guarantee precise identification. The tool also provides institution resolution that chronicles both current and past affiliations, enabling agents to construct accurate career timelines. In addition, it supplies academic work retrieval—a curated set of journal articles, letters, and research papers associated with each candidate author. To enrich the context further, citation analysis is included: h‑index values, total citation counts, and other impact metrics are surfaced to help agents gauge scholarly influence.

The server’s design prioritizes AI‑agent friendliness. Data structures are deliberately lightweight, containing only the fields essential for disambiguation and reasoning. This yields fast responses and reduces cognitive load on language models. The output is fully structured—JSON‑like payloads that can be parsed directly by an LLM, avoiding the need for additional text extraction steps. Moreover, the server offers smart filtering options: queries can be limited to journal‑only works, enforce citation thresholds, or apply temporal windows. These filters enable agents to tailor responses to specific user intents, such as “show me recent publications” or “list works with more than 50 citations.”

In real‑world scenarios, the OpenAlex MCP Server becomes a cornerstone for research workflows. Academic assistants can now answer queries like “Who is the most cited researcher named ‘J. Smith’ in computer science?” or “List all publications by Dr. María López from 2015 to 2020.” Because the server respects OpenAlex’s rate limits and manages HTTP clients efficiently, developers can integrate it into high‑throughput pipelines without risking service disruption. Its MCP best practices—built on FastMCP and compliant with official guidelines—ensure seamless discovery by any MCP‑compatible client, from Claude Desktop to OpenAI’s agents.

Overall, this MCP server delivers a unique advantage: it combines authoritative scholarly data with AI‑optimized interfaces, turning raw bibliographic information into actionable insights for developers and end users alike. By abstracting the complexities of author disambiguation, it frees AI agents to focus on higher‑level reasoning and user interaction.