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MCP Cornell Resume

@johndezr

代码仓库

基本信息

分类

记忆与知识

传输方式

stdio

作者

johndezr

提交者

John Galindez

关于 MCP Cornell Resume

A Model Context Protocol (MCP) server that automatically generates Cornell-style study notes and summaries from the conversational context, with RAG active recall question generation and Notion integration.

#notion#cornell#resume

MCP Cornell Resume 配置

使用下面的配置,将此服务器添加到你的 MCP 客户端。

{
  "mcpServers": {
    "resume_to_notion": {
      "command": "/path-to-uv/uv",
      "args": [
        "--directory",
        "/path-to-project/mcp-cornell-resume",
        "run",
        "main.py"
      ]
    }
  }
}

MCP Cornell Resume 工具

工具列表暂未提供

当前收录暂时没有可展示的工具列表，不代表服务没有工具。请查阅项目文档了解支持的能力。

工具是从 README 中自动提取的。维护者可以在 ## Tools 标题下列出工具,即可填充这部分内容。

概览

What is Mcp Cornell Resume?

An MCP server that generates Cornell-style study notes and active recall questions from conversational context. It uses RAG with Pinecone for context retrieval and automatically saves formatted summaries to a Notion database. Designed for users who want to turn chat history into structured study materials.

How to use Mcp Cornell Resume?

Set up by cloning the repository, installing dependencies with uv, and configuring environment variables (OpenAI, Pinecone, Notion API keys). Add the server to an MCP‑compatible client (e.g., Claude Desktop) using a JSON configuration pointing to uv run main.py. The tool save_resume_to_notion accepts a text string and returns a Notion page ID.

Key features of Mcp Cornell Resume

  • Real-time Cornell-style note generation from chat conversation history
  • Context-aware active recall question generation using vector similarity
  • Semantic search integration with Pinecone for relevant note retrieval
  • Automatic Notion database synchronization with proper block formatting
  • OpenAI-powered text processing and question generation

Use cases of Mcp Cornell Resume

  • Convert long chat conversations into structured Cornell notes for review
  • Generate active recall questions to test understanding of discussed topics
  • Maintain a searchable, organized knowledge base in Notion from chat sessions
  • Retrieve semantically related past notes while taking new ones

常见问题

1

What are the required dependencies and accounts?

Python 3.13+, the `uv` package manager, and accounts for OpenAI, Pinecone, and Notion. API keys must be stored in a `.env` file.

2

How does the data flow through the server?

The client sends chat text to `save_resume_to_notion`. OpenAI generates embeddings, Pinecone retrieves semantically similar notes, OpenAI creates a Cornell summary using that context, and the result is stored in both Pinecone and Notion. The Notion page ID is returned.

3

Where is my data stored?

Note content is saved to your Notion database with formatted blocks. Vector embeddings are stored in a Pinecone index for future context retrieval.

4

What are the current limitations?

The finite LLM context window (typically 8k–32k tokens) means very long conversations may lose information. Multiple integrations add latency, and Notion‑Pinecone sync is complex.

5

How are API keys handled?

All keys are stored in a `.env` file and never exposed in error messages. Input validation and access controls are applied according to Notion workspace permissions.

评论

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