Annota — AI-Powered Paper Annotation Assistant

Turn your PDF library into an intelligent research assistant.

AI reads your papers, highlights key findings, explains formulas, and writes structured notes — all saved back to your reference manager.

Features · Quick Start · Usage Examples · Screenshots · Roadmap

What Can It Do?

You say...	AI does...
"高亮摘要中的发现结果" (Highlight findings in the abstract)	Reads the abstract, identifies findings, highlights them in green
"解释第3页的公式" (Explain the formulas on page 3)	Extracts the formula, adds an explanation as a note annotation
"写一份结构化阅读笔记" (Write a structured reading note)	Generates a note with contributions, methods, results, limitations — saved to your library
"以 MICRO 审稿人视角审阅" (Review as a MICRO reviewer)	Produces a structured review with scores and actionable feedback

AI reads the paper → understands content → creates precise annotations

AI generates a structured reading summary with key findings, methods, and conclusions

✨ Features

9 MCP Tools

Tool	What it does
`search_zotero_items`	Search by title / author / key
`list_zotero_items`	Browse recent items
`get_item_metadata`	Get authors, year, venue, DOI
`get_pdf_text_bulk`	Extract full text (no coords, fast)
`get_pdf_layout_text`	Extract text + precise coordinates
`list_annotations`	View existing annotations
`create_pdf_annotation`	Create highlight / underline
`batch_annotate`	Create multiple annotations at once
`add_child_note`	Add a note to any item

3 Claude Code Skills (Slash Commands)

Command	Function
`/annota-annotate`	Smart annotation with semantic color coding
`/annota-summarize`	Structured reading notes saved to your library
`/annota-review`	Simulated peer review with scoring rubric

Smart Design

Two-phase workflow — Reads full text first (cheap), then only gets coordinates for target sentences (precise). Reduces context usage by 63–80%.
Auto-skip references — Detects "References" section and skips it. A 21-page paper extracts only 13 pages.
Batch annotations — Creates 10 highlights in 1 API call instead of 10.
Friendly errors — Write failures return helpful messages instead of crashing.

🚀 Quick Start (3 Minutes)

Step 1: Clone & Install

git clone https://github.com/dengls24/annota.git
cd annota

python -m venv .venv

# Windows:
.venv\Scripts\activate
# macOS / Linux:
# source .venv/bin/activate

pip install pymupdf mcp

Step 2: Configure Claude Code

Add to ~/.claude.json (or via Claude Code Settings > MCP Servers):

Windows:

{
  "mcpServers": {
    "annota": {
      "command": "C:/path/to/annota/.venv/Scripts/python.exe",
      "args": ["C:/path/to/annota/annota/server.py"],
      "env": {
        "ZOTERO_DATA_DIR": "C:/Users/YourName/Zotero"
      }
    }
  }
}

macOS / Linux:

{
  "mcpServers": {
    "annota": {
      "command": "/path/to/annota/.venv/bin/python",
      "args": ["/path/to/annota/annota/server.py"],
      "env": {
        "ZOTERO_DATA_DIR": "/Users/YourName/Zotero"
      }
    }
  }
}

Finding your Zotero data directory:

Windows: Zotero → Edit → Settings → Advanced → Data Directory Location (default: C:\Users\YourName\Zotero)

macOS: Zotero → Settings → Advanced → Data Directory Location (default: ~/Zotero)

Linux: default ~/Zotero

Step 3: Use It

Just talk to Claude naturally:

# One command to read a full paper:
/annota-read "path/to/paper.pdf"

# Or natural language:
# Highlight the findings in this paper's abstract in green
"/Users/yourname/Zotero/storage/ABCD1234/paper.pdf"

Or use slash commands:

/annota-read "path/to/paper.pdf"
/annota-annotate "path/to/paper.pdf"
/annota-summarize "path/to/paper.pdf"
/annota-review "path/to/paper.pdf" ISCA

macOS path tip: Drag a file from Finder into the terminal to get its full path, or right-click → "Copy as Pathname".

(Optional) Install Skills Globally

# Make skills available in all projects
cp -r .claude/skills/ ~/.claude/skills/

📖 Usage Examples

Example 1: Highlight Key Findings

Input:

把这篇论文摘要中的发现结果用绿色标出来
(Highlight the findings in this paper's abstract in green)
"E:\Zotero\storage\ABCD1234\Song et al. - 2025 - AI washing.pdf"

Result:

AI identifies findings in the abstract and highlights them in green

Example 2: Annotate Hypotheses & Theories

Input:

标注论文中的假设(H1, H2)，并用中文解释每个假设的理论基础
(Annotate the hypotheses (H1, H2) and explain the theoretical basis of each in Chinese)

Result:

Hypotheses highlighted in yellow, with Chinese explanation notes for the underlying theory

Example 3: Explain Formulas

Input:

解释论文中的核心公式，添加中文注释
(Explain the key formulas in this paper, add Chinese annotations)

Result:

DID model formula annotated with variable explanations in Chinese

Example 4: Policy Implications & Conclusion Notes

Input:

标注结论部分的政策启示，添加中文总结笔记
(Highlight policy implications in the conclusion, add a Chinese summary note)

Result:

Conclusion highlighted with a structured policy implications note

Example 5: Full Paper Reading Notes

Input:

/annota-summarize "path/to/paper.pdf"

Result:

AI generates a complete reading summary: topic, research question, method, key findings, and implications

Example 6: Detailed Paragraph-by-Paragraph Notes

Input:

逐段阅读这篇论文，为每个重要段落添加中文批注
(Read this paper paragraph by paragraph, add Chinese annotations to each important section)

Result:

Each important paragraph gets a Chinese annotation explaining the content

Example 7: The AI Workflow in Action

Here's what Claude Code looks like when processing a paper:

Claude creates a task list, reads the PDF, and calls MCP tools to create annotations step by step

🎨 Color Convention

Color	Code	Use for
🟡 Yellow	`#ffd400`	Default / general highlights
🟢 Green	`#28CA42`	Results, findings, data
🔵 Blue	`#2EA8E5`	Methods, definitions, algorithms
🔴 Red	`#ff6666`	Limitations, issues, problems
🟣 Purple	`#a28ae5`	Contributions, novelty

⚡ How It Handles Large PDFs

For papers >10 pages, a two-phase workflow avoids context overflow:

Phase 1 — Understand (lightweight)
  get_pdf_text_bulk(pdf, skip_refs=True)
  → Full text without coordinates
  → AI identifies which sentences to annotate

Phase 2 — Annotate (precise)
  get_pdf_layout_text(pdf, target_page_only)
  → Coordinates for 1–2 target pages
  batch_annotate(pdf, all_annotations)
  → Write everything in one call

Real-world performance:

Paper	Pages	Old approach	New approach	Savings
Conference paper	2 pages	41 KB coords	15 KB text	63%
Journal article	21 pages	21 pages extracted	13 pages (refs skipped at p.13)	38%
Survey paper	19 pages	19 pages extracted	10 pages (refs skipped at p.10)	47%

📁 Project Structure

annota/
├── annota/                        # MCP Server (Python)
│   ├── server.py                  # 9 tool registrations
│   ├── zotero_db.py               # SQLite read/write layer
│   ├── pdf_tools.py               # PyMuPDF text extraction
│   └── config.py                  # Constants & configuration
├── .claude/skills/                # Claude Code Skills
│   ├── annota-annotate/SKILL.md   # /annota-annotate
│   ├── annota-summarize/SKILL.md  # /annota-summarize
│   └── annota-review/SKILL.md     # /annota-review
├── docs/                          # Design documents
│   ├── annota-guide.md            # Usage guide (CN)
│   ├── large-pdf-design.md        # Large PDF handling design
│   ├── dev-notes.md               # Pitfalls & solutions
│   └── commercial-plan.md         # Commercialization plan
├── assets/                        # Screenshots
└── README.md

⚠️ Known Limitations & Disclaimer

Database Direct Access: Annota writes annotations directly to the Zotero SQLite database, which bypasses Zotero's internal consistency mechanisms. This is a design choice to enable fully offline, local-first annotation workflows without depending on external services. Users are responsible for their own database — please back up your zotero.sqlite before use. We plan to migrate to the official Zotero Web API / Local API in future versions.

Limitation	Workaround	Planned Fix
Direct SQLite write (not officially supported)	Back up your database before use	Migrate to Zotero Local API / Web API
Write ops need Zotero closed	Close Zotero before annotating	Local API bridge
References detection is heuristic	Pass `skip_refs=False` if needed	Improve heuristics
Tested primarily on Windows	Should work on macOS/Linux — paths auto-detected	Community testing welcome

🗺 Roadmap

Zotero Local API / Web API — Migrate from direct SQLite to official API for safer writes
More skills — /compare-papers, /extract-tables, /literature-map
Prompt template marketplace — Share and reuse annotation rules
Team features — Shared annotation standards for lab groups
Multi-backend — Support Adobe Acrobat, Endnote, and other PDF tools

🤝 Contributing

Issues and PRs are welcome! If you have ideas for new skills or tools, please open an issue.

📄 License

MIT — Use it freely for research and commercial projects.

Built with MCP + Claude Code

If this project helps your research, consider giving it a ⭐

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Repository files navigation

Annota — AI-Powered Paper Annotation Assistant

What Can It Do?

AI reads the paper → understands content → creates precise annotations

✨ Features

9 MCP Tools

3 Claude Code Skills (Slash Commands)

Smart Design

🚀 Quick Start (3 Minutes)

Step 1: Clone & Install

Step 2: Configure Claude Code

Step 3: Use It

(Optional) Install Skills Globally

📖 Usage Examples

Example 1: Highlight Key Findings

Example 2: Annotate Hypotheses & Theories

Example 3: Explain Formulas

Example 4: Policy Implications & Conclusion Notes

Example 5: Full Paper Reading Notes

Example 6: Detailed Paragraph-by-Paragraph Notes

Example 7: The AI Workflow in Action

🎨 Color Convention

⚡ How It Handles Large PDFs

📁 Project Structure

⚠️ Known Limitations & Disclaimer

🗺 Roadmap

🤝 Contributing

📄 License

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Name		Name	Last commit message	Last commit date
Latest commit History 6 Commits
.claude/skills		.claude/skills
annota		annota
assets		assets
docs		docs
.gitignore		.gitignore
LICENSE		LICENSE
README.md		README.md
pyproject.toml		pyproject.toml
task.md		task.md

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Repository files navigation

Annota — AI-Powered Paper Annotation Assistant

What Can It Do?

AI reads the paper → understands content → creates precise annotations

✨ Features

9 MCP Tools

3 Claude Code Skills (Slash Commands)

Smart Design

🚀 Quick Start (3 Minutes)

Step 1: Clone & Install

Step 2: Configure Claude Code

Step 3: Use It

(Optional) Install Skills Globally

📖 Usage Examples

Example 1: Highlight Key Findings

Example 2: Annotate Hypotheses & Theories

Example 3: Explain Formulas

Example 4: Policy Implications & Conclusion Notes

Example 5: Full Paper Reading Notes

Example 6: Detailed Paragraph-by-Paragraph Notes

Example 7: The AI Workflow in Action

🎨 Color Convention

⚡ How It Handles Large PDFs

📁 Project Structure

⚠️ Known Limitations & Disclaimer

🗺 Roadmap

🤝 Contributing

📄 License

About

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Resources

License

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Releases 1

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