Arxivloader MCP Server

Arxivloader MCP Server and Client

The Arxivloader MCP server addresses a common bottleneck for AI developers: the need to surface up‑to‑date scientific literature in conversational agents. By exposing an MCP endpoint that accepts natural language queries and returns structured metadata from arXiv, the server lets assistants like Claude pull research papers on demand without manual browsing. This capability is especially valuable for domains where staying current with cutting‑edge studies—such as medicine, AI safety, or quantum computing—is critical.

At its core, the server implements a lightweight microservice that listens for MCP messages, interprets the query string, and forwards it to arXiv’s public API. The retrieved results are then packaged into a JSON payload that the client can consume. On the client side, a Streamlit UI provides an intuitive web interface: users type a title or keyword set, hit submit, and the client streams back a list of matching papers with titles, authors, abstracts, and publication dates. The integration with LangChain and Groq enables optional semantic parsing or ranking of results, allowing the assistant to surface the most relevant studies quickly.

Key features include:

• MCP‑based communication that keeps the server lightweight and language‑agnostic.
• Query‑to‑paper mapping using arXiv’s search API, ensuring up‑to‑date results.
• Optional LangChain pipelines for advanced filtering or summarization before presenting to the user.
• Streamlit front‑end that can be embedded in larger dashboards or deployed as a standalone microservice.

Typical use cases span academic research assistants, legal compliance bots that need to cite recent studies, or industry R&D tools that require rapid literature reviews. For example, a healthcare AI can ask for the latest papers on “medical claim processing” and instantly receive curated references, which can then be fed into a downstream summarization model.

Integrating this server into an AI workflow is straightforward: the assistant sends an MCP request with the user’s query, receives a structured list of papers, and can optionally invoke further tools (e.g., summarization or citation generation). The server’s modular design means it can be swapped out for other scholarly databases with minimal changes, while the MCP interface guarantees compatibility across diverse AI platforms.