HuggingFace Daily Papers MCP Server

The HuggingFace Daily Papers MCP Server solves the recurring challenge of keeping AI assistants up‑to‑date with the latest research released on HuggingFace. Every day, a handful of new papers are added to the platform; manually parsing the website or scraping each page is tedious and error‑prone. This server automates that process, exposing the data through MCP tools and resources so that developers can effortlessly feed fresh research into conversational agents, knowledge bases, or recommendation systems.

At its core, the server scrapes the HuggingFace papers page for a given date—today, yesterday, or any specific day—and returns a structured JSON payload containing the paper’s title, authors, abstract, tags, vote count, submitter, and direct links to the HuggingFace page and the PDF on ArXiv. By leveraging MCP’s tool interface, an assistant can invoke , , or with a simple date string, receiving a ready‑to‑consume list of papers. The resource URLs ( and ) allow clients to fetch the same data without executing a tool, supporting both synchronous and asynchronous workflows.

Key capabilities include:

• Time‑based querying: Retrieve papers for any historical date or automatically fetch the latest batch.
• Rich metadata extraction: Authors, abstract, tags, votes, and submitter information are all captured for contextual relevance.
• Dual access paths: Tools for on‑demand queries and resources for static data retrieval.
• ArXiv integration: PDF links are resolved through ArXiv, ensuring that full texts are available even if the HuggingFace page is limited.
• Robust error handling and logging: Guarantees reliability in production environments, with clear diagnostics for failures.

Real‑world scenarios benefit from this server include:

• Research assistants that summarize new papers or answer domain‑specific questions.
• Curriculum builders that automatically curate recent studies for educational content.
• Recommendation engines that surface cutting‑edge models or datasets to developers.
• Data pipelines where fresh research feeds downstream analytics or trend‑analysis modules.

Integrating the server into an AI workflow is straightforward: add it to your MCP configuration, and any Claude or other MCP‑compatible assistant can call the provided tools. The assistant receives a concise, human‑readable summary of each paper, enabling natural language interactions such as “Show me the latest HuggingFace papers on diffusion models” or “Give me a quick overview of yesterday’s releases.”

The standout advantage lies in its automation and consistency. By centralizing the scraping logic behind a protocol‑compliant interface, developers avoid duplicating code across projects and can trust that the data is uniformly formatted. This eliminates manual parsing, reduces latency in knowledge updates, and ensures that AI assistants always have the most current research at their fingertips.