PapersWithCode MCP Server

Overview

The mcp‑paperswithcode server bridges AI assistants with the rich ecosystem of academic research hosted by PapersWithCode. By exposing a comprehensive set of tools that mirror the public API, it lets Claude and other MCP‑enabled assistants search for papers, retrieve metadata, extract readable content from PDFs or HTML pages, and explore the surrounding code repositories, datasets, benchmarks, and tasks. This capability turns an AI assistant into a real‑time research companion that can surface the latest findings, trace implementation details, and suggest related work—all without leaving the conversational interface.

For developers building AI‑powered research workflows, this server solves a key pain point: the lack of an easy, structured way to query scholarly literature and its associated artifacts. Instead of manually navigating the PapersWithCode website or writing custom scrapers, a developer can invoke high‑level commands such as , , or . Each tool returns JSON‑structured data that can be fed directly into downstream reasoning, summarization, or code generation tasks. The result is a smoother integration of up‑to‑date research into prototypes, experiments, or educational tools.

Key features include:

• Paper discovery and retrieval: Search by title, abstract, or ArXiv ID; fetch full metadata; pull readable text from PDFs/HTML URLs.
• Contextual linkage: List benchmark results, tasks, methods, code repositories, and datasets tied to a paper, enabling the assistant to explain performance claims or provide runnable examples.
• Research area navigation: Query research areas, their tasks, and associated literature to help users situate a paper within its broader field.
• Author and conference exploration: Find authors by name, list their publications, and retrieve conference proceedings—all useful for mapping collaboration networks or tracking venue trends.
• Fine‑grained filtering: Optional parameters on search tools let developers narrow results to specific conferences, datasets, or tasks, ensuring relevance and reducing noise.

Real‑world scenarios that benefit from this server include:

• Academic assistants: Students or researchers ask an AI to find the most recent papers on a niche topic, read summaries, and clone associated code for experimentation.
• Experiment automation: A data scientist can ask the assistant to list all datasets used in a paper, pull them via another MCP tool, and automatically generate evaluation scripts.
• Curriculum design: Educators query conference proceedings to curate up‑to‑date lecture materials, complete with code demos sourced directly from PapersWithCode.
• Product research: Engineers explore state‑of‑the‑art methods in a domain, retrieve benchmark scores, and quickly prototype the highlighted techniques.

By integrating with existing MCP workflows, developers can compose multi‑step reasoning chains: search → retrieve → parse → generate code or explanations. The server’s uniform, declarative interface removes the need for custom parsing logic and ensures that AI assistants can reliably access high‑quality research artifacts, thereby accelerating innovation across academia and industry.