Cornell Resume MCP Server

The MCP: Cornell Resume server is designed to transform conversational data into structured, study‑ready content that can be immediately consumed by AI assistants and productivity tools. It tackles the common problem of fragmented chat logs that are difficult to review, reference, or share. By automatically generating Cornell‑style notes—complete with cue columns, summaries, and keywords—the server turns raw dialogue into a compact learning artifact that preserves context while eliminating noise. This is particularly valuable for developers building knowledge‑management workflows, where AI agents need to retrieve and build on past conversations without re‑reading entire histories.

At its core, the server exposes a single tool, , that accepts a text blob of the current chat window. It then orchestrates a multi‑stage pipeline: OpenAI embeddings capture semantic meaning, Pinecone performs similarity searches to surface related notes, and a second LLM pass synthesizes the conversation into a clean Cornell template. The resulting block‑formatted page is automatically pushed to a Notion database, making the notes instantly searchable and shareable across teams. Developers can hook this tool into any MCP‑compatible client, allowing agents to “save” a session with a single command and later reference it in follow‑up interactions.

Key capabilities include:

• Real‑time Cornell note generation that respects the structure of cue, note, and summary columns.
• Context‑aware question generation powered by vector similarity, enabling active recall prompts that stay relevant to the current discussion.
• Semantic search via Pinecone, ensuring new notes are enriched with related content from prior sessions.
• Seamless Notion integration, automatically creating pages and formatting blocks without manual copy‑paste.
• OpenAI‑driven text processing, allowing the system to adapt to different languages, styles, and domain vocabularies.

Typical use cases span education tech, where tutors can generate study aids from tutoring sessions; corporate knowledge bases, where meeting transcripts are turned into searchable notes; and personal productivity tools that keep a running log of learning moments. By embedding the server into an AI assistant’s workflow, developers can provide users with instant, structured summaries that reduce cognitive load and improve retention.

The server’s design emphasizes low friction integration: a single MCP configuration entry launches the Python process, and the tool returns the Notion page ID for downstream actions. Its modular architecture means developers can swap out embedding providers, switch from Pinecone to another vector store, or adjust the Notion schema with minimal code changes. The result is a robust, AI‑first note‑taking service that elevates conversational data into actionable knowledge.