$ loading_
Search, extract, and manage arXiv paper metadata and collections.
This MCP tool is described as searching and managing arXiv papers, with no declared API keys or remote endpoints, and no obvious high-risk red flags are present in the provided materials. However, it is flagged as executing code and comes from a third-party registry with weak adoption and unclear maintenance, so cautious use is advisable.
The materials explicitly state that no keys or environment variables are required, and there is no request for API tokens, account credentials, or other sensitive authentication data, so credential exposure appears limited.
Although no remote endpoint is declared in the metadata, its core function of searching and discovering arXiv papers would normally require network requests and may send user queries to external services. The materials do not specify the exact domains, data scope, or privacy boundaries, creating transparency concerns.
The system checks flag this tool as executes-code, indicating that it runs code or processes in the local environment; this is a common MCP capability, but the lack of README or clear capability boundaries makes it impossible to verify how constrained that execution surface is.
The description mentions managing papers and browsing saved collections, which implies some ability to save, read, or organize data locally. The materials do not state storage locations, accessible file scope, or whether access is limited to the tool’s own data directory, so data boundaries should be treated cautiously.
A public GitHub repository is available for review, which is a positive factor that lowers risk; however, it comes from a third-party registry, has no declared license, shows 0 stars, unknown maintenance status, and lacks a README, so overall supply-chain transparency and maturity are limited.
Copy the install command and let the AI configure it · recommended for beginners
No copy-paste install info for "Research MCP Server" yet — see the docs or source repo.
Search arXiv for the latest papers on retrieval-augmented generation, and list title, authors, abstract, and links.
A list of relevant papers with key metadata and clickable links.
Group my saved papers on LLM evaluation by topic and identify the most cited ones.
An organized view of saved papers with top-cited recommendations.
Given this arXiv paper link, extract its metadata and summarize its core contribution.
Paper metadata plus a concise summary of its main contribution.
Search and retrieve arXiv papers by topic, author, category, or ID.
Search, verify, and read arXiv papers with fewer hallucinated citations.
Search, read, and extract key insights from arXiv papers via AI assistants.
Search, read, and summarize arXiv papers for faster research and study.
Search arXiv and download papers through MCP for faster research access.
Search arXiv papers, fetch metadata, browse categories, and read full text.