Research
Provides an MCP server for accessing academic paper data from arXiv and serving prompt templates for large language models.
概要
This project offers a practical implementation of the Model Context Protocol (MCP) server, designed to facilitate building rich-context AI applications. Developed as a hands-on exercise from DeepLearning.AI's 'Build Rich-Context AI Apps with Anthropic' course, it demonstrates how to standardize LLM access to external tools and data. The server specifically exposes functionalities for searching and retrieving academic papers from arXiv, manages research-related resources, and supplies structured prompt templates, enabling LLMs to synthesize and summarize complex research topics. Built with FastMCP, it features a robust client-server architecture with Docker and Procfile support for easy deployment.
主な機能
- Resource endpoints for listing topics and retrieving detailed paper data.
- Provides prompt templates for LLMs to synthesize and summarize research.
- Client-server architecture built using the FastMCP Python package.
- Standardized tool access for searching arXiv and extracting paper information.
- Docker and Procfile support for versatile deployment.
- 0 GitHub stars
ユースケース
- Generating structured prompts for LLMs to perform research synthesis tasks.
- Serving as a backend for MCP-compatible AI clients like Claude Desktop or MCP Inspector.
- Facilitating the retrieval and summarization of academic papers from arXiv.
