second-brain MCP Server

A self-maintaining personal knowledge database — powered by MCP, DuckDB, and biological memory models.

For anyone who saves more papers, notes, and figures than they could ever re-read. second-brain turns everything you capture into a database that maintains itself — auto-linking related notes, compressing what you stop reading, and keeping every figure searchable by its content. What you saved a year ago is still one query away, at a fraction of the token cost.

Why Does This Exist?

The One-Command Demo

save_article("https://arxiv.org/abs/2405.01234")
  ↓
• /abs/ auto-upgraded to /html/ — full paper, not just abstract
• Full text converted to Markdown
• All figures downloaded + OCR'd by Claude Vision
• Semantic embeddings computed
• Auto-linked to related notes already in your vault   ← auto-wikilinks
• Stored in 30-resources/ — queryable immediately

search_figures("UMAP cluster batch correction")
  ↓
• Returns the exact figure from the exact paper
• Works across your entire saved literature library

What Makes It Different

flowchart LR
    subgraph input["📥 Any Content Source"]
        A1["arXiv / PubMed paper"]
        A2["Web article / blog"]
        A3["Local PDF / DOCX"]
        A4["Personal note"]
    end

    subgraph core["⚙️ second-brain-mcp"]
        B1["Markdown note<br/>30-resources/"]
        B2["Figure OCR<br/>+ VLM description"]
        B3["Semantic embedding<br/>+ auto-wikilinks"]
        B4["Ebbinghaus score<br/>ranking"]
        B5["PNG snapshots<br/>60–90% token reduction"]
    end

    subgraph query["🔍 Queryable Knowledge"]
        C1["search_figures<br/>'UMAP melanocyte'"]
        C2["search_notes<br/>'batch correction scRNA'"]
        C3["get_context<br/>top-20 relevant notes"]
    end

    input --> core
    B1 --> B2
    B1 --> B3
    B3 --> B4
    B4 --> B5
    B2 --> C1
    B3 --> C2
    B4 --> C3

Eight things most self-hosted memory tools can't do — combined in one:

... View full README on GitHub