Papers with Code is a platform that connects machine learning research papers with their associated code implementations. It helps researchers and developers discover state-of-the-art ML methods with working code, making AI research more accessible and reproducible.
Enables structured extraction of methods and reproducibility heuristics from academic papers, allowing AI agents to obtain metadata, full text, structured methods, code repository discovery, and a no-clone reproducibility verdict from a paper URL.
8
MIT
Why this server?
Enables access to research papers paired with their code implementations, helping discover reproducible AI research with available source code.
Enables real-time tracking of AI/LLM research progress by searching and aggregating content from arXiv, GitHub, Hugging Face, and Papers with Code. Supports intelligent search, automated daily/weekly research summaries, and covers 15+ AI research areas with smart caching.
8
MIT
Why this server?
Fetches trending ML/AI research papers from Papers with Code.
A unified MCP server for academic paper discovery, citation exploration, and research intelligence workflows over multiple scientific knowledge sources.
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1
MIT
Why this server?
Allows searching Papers With Code papers, datasets, and code through Dataville.
Enables searching and exploring 340 million academic papers from 15+ databases with semantic search, citation graphs, and knowledge graph connections for biomedical entities.
1
MIT
Why this server?
Provides access to datasets from Papers with Code, though currently degraded due to API shutdown.
Open-source research engine that extracts structured knowledge from any topic via MCP, enabling AI assistants to get verified, scored claims and entity graphs from live sources.
