Overview of the OpenReview MCP Server

The OpenReview MCP server bridges AI assistants with the rich ecosystem of academic research hosted on OpenReview. It solves a common pain point for data scientists, researchers, and developers: programmatically accessing conference submissions, author profiles, and publication metadata without wrestling with OpenReview’s raw API. By exposing a set of high‑level tools and search capabilities, the server lets AI agents retrieve scholarly content in formats that are immediately useful for analysis, summarization, or downstream coding tasks.

At its core, the server offers a suite of search and retrieval functions that mirror everyday research workflows. Users can look up an author by email, pull all of their papers, or query entire conferences for specific years and venues. Keyword search spans multiple conferences simultaneously, enabling rapid exploration of topical trends across ICML, ICLR, and NeurIPS. The ability to export results as JSON or PDF streamlines the handoff between AI assistants and human analysts, allowing quick inspection of metadata or automated ingestion into data pipelines.

Developers integrating this MCP server benefit from its straightforward configuration: a single file holds OpenReview credentials, and the server can be added to Claude Code with a minimal command. Once registered, AI assistants can invoke tools like , , and directly from natural language queries. The server’s responses are structured, making it trivial for an assistant to feed the data into downstream models or scripts.

Real‑world use cases include literature reviews, citation analysis, and trend monitoring. For instance, a researcher can ask an AI assistant to find all papers on “time series token merging” from ICLR and ICML 2025, then automatically download the PDFs for a deep dive. Similarly, data engineers can pull publication lists to populate knowledge graphs or generate author‑impact metrics. The server’s export feature ensures that these results are ready for presentation, reporting, or further computational processing.

Unique advantages of the OpenReview MCP server lie in its tight integration with a leading academic platform and its focus on conference‑centric data. Unlike generic web scrapers, it respects OpenReview’s API rate limits and authentication flow, providing reliable access to up‑to‑date content. Its lightweight design means it can run locally or in the cloud, making it an ideal component for AI‑driven research assistants that need quick, trustworthy access to scholarly information.