
Arxiv Paper MCP
The arXiv Research Assistant MCP Server is a local server built on Python and the FastMCP framework, specifically designed to interact with the arXiv.org paper database. It provides functions such as keyword search, getting the latest papers by category, querying paper details, retrieving author papers, and supports generating paper summaries and comparison prompts to help users efficiently explore academic literature.
Python
3.6K
2 points

Zotero MCP Server
Zotero MCP Server is a server prototype based on the Pyzotero API that allows users to search for all notes in the Zotero library through queries and obtain the complete content of specific items. The project provides search and retrieval functions and supports integration with the 5ire MCP client.
Python
3.7K
2.5 points

Zotero
The Zotero MCP Connector is a tool for integrating the local Zotero literature library with the Claude desktop version, enabling direct reading and access through the local Web API.
Python
3.5K
2.5 points

Citeassist (Citation Retrieval)
An MCP server that provides BibTeX - formatted reference data, integrating CiteAs and Google Scholar resources
Python
3.4K
2.5 points

Zotero MCP Server
The Zotero MCP Server is a model context protocol service integrated with Zotero, allowing AI applications to access and operate on the Zotero literature library, supporting functions such as search, citation, and addition of entries.
Python
3.2K
2 points
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Markdownify MCP
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Unity
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Figma Context MCP
Framelink Figma MCP Server is a server that provides access to Figma design data for AI programming tools (such as Cursor). By simplifying the Figma API response, it helps AI more accurately achieve one - click conversion from design to code.
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Gmail MCP Server
A Gmail automatic authentication MCP server designed for Claude Desktop, supporting Gmail management through natural language interaction, including complete functions such as sending emails, label management, and batch operations.
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Context7
Context7 MCP is a service that provides real-time, version-specific documentation and code examples for AI programming assistants. It is directly integrated into prompts through the Model Context Protocol to solve the problem of LLMs using outdated information.
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