zotero-research-mcp 0.1.0

AI-powered Zotero research assistant — 29 MCP tools for literature search, reading analysis, citation management, and review writing.

Zotero Research Assistant

Turn your Zotero library into an AI-powered research engine.

Search by meaning, discover related papers across 200M+ works, get personalized reading recommendations, and manage your entire academic workflow — all through natural language.

Works with Cursor, Claude Desktop, Cherry Studio, Trae, OpenAI Codex CLI, and any MCP-compatible client.

Highlights

29 MCP Tools	One intent per tool — LLMs always pick the right one
Hybrid RAG Search	Keyword + semantic (bge-m3, 100+ languages) + cross-encoder reranking
Multi-Source Discovery	OpenAlex + CrossRef + Semantic Scholar in parallel, Three-Index Verification to prevent fabricated citations
Citation Network Expansion	Corpus-First strategy + forward/backward citations + OpenAlex Related Works
Anti-Hallucination	Zero-fabrication policy with [MATERIAL GAP] structural tags; every paper has a verifiable source link
Personalized Recommendations	Learns from your reading activity and annotations to suggest what to read next
Literature Review Generator	Select papers → extract evidence with citations → AI synthesizes thematic review
Smart Tag Suggestions	Auto-analyze metadata to recommend methodology/domain/data tags (confirm before apply)
Argument Finder	Find supporting & opposing evidence for your thesis from your library
CNKI Integration	Optional Chinese literature search with journal-level tags (CSSCI/PKU Core/CSCD)
OA PDF Waterfall	arXiv → Unpaywall → OpenAlex → S2 → CORE → PMC automatic full-text retrieval
Write Safety	All destructive operations require explicit user approval (dry-run by default)

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Table of Contents

• Features
• Requirements
• Quick Start
• Client Setup
  • Cursor
  • Claude Desktop
  • Cherry Studio
  • Trae
  • OpenAI Codex CLI
  • Other MCP Clients
• Example Prompts
• MCP Tools (29)
• Configuration
• CNKI Setup (Optional)
• Updating
• Troubleshooting
• Architecture
• Development
• Acknowledgments
• License

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Features

Local Library Intelligence

• Hybrid search — Zotero keyword search + ChromaDB semantic search, merged with Reciprocal Rank Fusion; fallback to Zotero full-text index
• Filter-only search — list papers by year, tags, or collection with an empty query
• Cross-encoder reranking — optional ms-marco-MiniLM-L-6-v2 for higher precision
• Multilingual — BAAI/bge-m3 embedding (1024-dim, 100+ languages including Chinese and English)
• Page-level traceability — retrieved passages include exact PDF page numbers
• Full-text & outline — read complete paper text or PDF table of contents
• Incremental index sync — version-based diff; auto-sync on MCP startup

Online Literature Discovery

• Multi-source search — queries OpenAlex, CrossRef, and Semantic Scholar in parallel with publisher-diverse ranking
• Corpus-First strategy — when a paper's reference list is available, the system expands citation networks from those known references as the PRIMARY search strategy, yielding the most relevant results
• Discipline filtering — optional fields_of_study parameter constrains results to relevant academic fields (Business, Economics, Sociology, etc.), preventing cross-domain noise
• Related paper discovery — provide a paper's title/abstract/keywords → automatically generates tiered pairwise queries → searches all sources → post-filters irrelevant results → returns deduplicated hits in a single call
• Three-Index Verification — every result with a DOI is cross-checked against CrossRef, OpenAlex, and Semantic Scholar; papers not findable in ANY index are filtered out to prevent fabricated citations
• Source verification — every returned paper includes a verifiable link (DOI URL, Semantic Scholar URL, or CNKI link) so users can independently check authenticity
• Anti-hallucination guardrails — structural [MATERIAL GAP] tags in tool outputs when search returns zero results; the AI is instructed to never fabricate citations and must report gaps honestly

CNKI (Chinese Literature)

• CNKI integration — optional Chinese journal search via browser automation (disabled by default, enabled on demand)
• Journal-level tags — search results include indexing status badges (CSSCI, PKU Core, CSCD, SCI, EI)
• Direct Zotero import — export papers from CNKI to Zotero without manual DOI lookup
• Paper detail extraction — full metadata (abstract, keywords, DOI, affiliations) from CNKI detail pages
• Smart pagination — AI proactively fetches more results when thorough coverage is needed

Reading Insight & Recommendations

• Reading status detection — heuristic classification (deep_read / browsed / unread) based on annotation count, notes, and PDF open history (Zotero 7 reader saves reading position, updating attachment timestamps)
• Personalized recommendations — identifies your most-engaged papers → queries OpenAlex Related Works + S2 Recommendations in parallel → deduplicates, excludes already-in-library → ranks by cross-seed frequency
• Focus topic extraction — surfaces your active research themes from recent reading tags
• Literature review generation — select multiple papers → extract relevant passages with page-level citations → structured output for AI to synthesize into a thematic review
• Smart tag suggestions — analyzes title/abstract to recommend methodology, domain, and data-type tags; matches against existing library tags; suggest-only (never auto-applies)
• Argument finder — given a thesis/claim, searches library for evidence grouped by stance (support/oppose/neutral); heuristic pre-classification with textual signals; designed for writing Discussion sections

Library Management

• Add papers — DOI, arXiv, ISBN, BibTeX, or publisher URL (ScienceDirect, Springer, Wiley, …)
• Open-access PDF waterfall — arXiv → Unpaywall → OpenAlex → Semantic Scholar → CORE → PMC
• Duplicate merge — find by DOI/title, merge with dry-run preview
• Annotations — search highlights across the library; create highlights on PDFs
• Write safety — all write/delete operations preview first; requires explicit user approval
• Hybrid Zotero mode — fast local reads + web API writes (when API key is set)

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Requirements

Component	Version / Note
Python	3.11 – 3.13
Zotero	7+ desktop app, running with local API enabled
MCP client	Cursor, Claude Desktop, Cherry Studio, Trae, Codex CLI, etc.
LLM	Any model with tool/function calling (Claude, GPT-4o, DeepSeek, Qwen, Gemini, …)
Disk	~2.5 GB for embedding model (bge-m3) on first run
Git	To clone this repository

Path tip: Install in a short path without spaces or non-ASCII characters, e.g. ~/zotero-research-agent (macOS/Linux) or C:\Dev\zotero-research-agent (Windows).

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Quick Start

1. Clone the repository

git clone https://github.com/qiobn/zotero-research-agent.git
cd zotero-research-agent

2. Install dependencies

Install uv (fast Python package manager) if not already present:

# macOS / Linux
curl -LsSf https://astral.sh/uv/install.sh | sh

# Windows (PowerShell)
irm https://astral.sh/uv/install.ps1 | iex

Create a virtual environment and install:

uv venv .venv --python 3.13      # use 3.12 or 3.11 if unavailable
uv pip install -e .

Verify installation:

# macOS / Linux
source .venv/bin/activate
python -c "from project_a_mcp.server import mcp; print('OK')"

# Windows (PowerShell)
.venv\Scripts\activate
python -c "from project_a_mcp.server import mcp; print('OK')"

First run downloads the embedding model (~2.3 GB). If download is slow, set HF_ENDPOINT=https://hf-mirror.com and retry.

3. Configure Zotero

Enable local API (required):

1. Open Zotero → Edit → Settings → Advanced
2. Check "Allow other applications on this computer to communicate with Zotero"
3. Verify: http://localhost:23119/api/ should return JSON

Create .env:

cp .env.example .env

Minimum for read-only mode (search, read, cite):

ZOTERO_LOCAL=true

For write operations (add papers, notes, tags, collections), also set your Zotero API key:

ZOTERO_LOCAL=true
ZOTERO_LIBRARY_ID=12345678
ZOTERO_API_KEY=your_api_key_here

4. Build the vector index (first time)

Ensure Zotero is running, then:

python scripts/index_library.py

This parses PDFs and stores embeddings in .chroma_db/ (local only, not committed to git). Typical time: ~3–5 min for 100 papers, ~10–15 min for 500 papers.

After the first run, the server auto-syncs incrementally on startup (ZRA_AUTO_SYNC=true).

5. Connect your AI client

See the Client Setup section below for your specific tool.

6. Test the connection

1. Start Zotero desktop
2. Open a new chat in your MCP client
3. Ask: "List all collections in my Zotero library"

If you see your collections, setup is complete.

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Client Setup

All clients use the same MCP server entry point. You need two paths:

Value	macOS / Linux	Windows
Python binary	<project>/.venv/bin/python	<project>\.venv\Scripts\python.exe
Working directory	<project> (full path)	<project> (full path)

Replace <project> with your clone path (e.g. /Users/you/zotero-research-agent or C:\Dev\zotero-research-agent).

Quick path helper (run inside the project folder):

# macOS / Linux
echo "$(pwd)/.venv/bin/python"

# Windows (PowerShell)
echo "$PWD\.venv\Scripts\python.exe"

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Cursor

Settings → MCP → Add new MCP server

Field	Value
Name	zra-mcp
Type	command (stdio)
Command	<project>/.venv/bin/python -m project_a_mcp.server

Or add to .cursor/mcp.json in your workspace:

{
  "mcpServers": {
    "zra-mcp": {
      "command": "/Users/you/zotero-research-agent/.venv/bin/python",
      "args": ["-m", "project_a_mcp.server"],
      "cwd": "/Users/you/zotero-research-agent"
    }
  }
}

Windows variant:

{
  "mcpServers": {
    "zra-mcp": {
      "command": "C:\\Dev\\zotero-research-agent\\.venv\\Scripts\\python.exe",
      "args": ["-m", "project_a_mcp.server"],
      "cwd": "C:\\Dev\\zotero-research-agent"
    }
  }
}

Restart Cursor after adding the config. The MCP tools will appear in Agent mode.

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Claude Desktop

Edit claude_desktop_config.json:

• macOS: ~/Library/Application Support/Claude/claude_desktop_config.json
• Windows: %APPDATA%\Claude\claude_desktop_config.json

{
  "mcpServers": {
    "zra-mcp": {
      "command": "/Users/you/zotero-research-agent/.venv/bin/python",
      "args": ["-m", "project_a_mcp.server"],
      "cwd": "/Users/you/zotero-research-agent"
    }
  }
}

Restart Claude Desktop. You should see the MCP tools icon (hammer) in the chat input area.

---

Cherry Studio

Settings → MCP Servers → Add → JSON mode:

{
  "mcpServers": {
    "zra-mcp": {
      "name": "zra-mcp",
      "type": "stdio",
      "isActive": true,
      "command": "/Users/you/zotero-research-agent/.venv/bin/python",
      "args": ["-m", "project_a_mcp.server"],
      "cwd": "/Users/you/zotero-research-agent"
    }
  }
}

Windows:

{
  "mcpServers": {
    "zra-mcp": {
      "name": "zra-mcp",
      "type": "stdio",
      "isActive": true,
      "command": "C:\\Dev\\zotero-research-agent\\.venv\\Scripts\\python.exe",
      "args": ["-m", "project_a_mcp.server"],
      "cwd": "C:\\Dev\\zotero-research-agent"
    }
  }
}

Configure an LLM under Settings → Model Services (DeepSeek, GPT-4o, Claude, Qwen, etc.). Enable the MCP toggle in the chat interface to activate tools.

For a detailed step-by-step guide (including screenshots), see docs/cherry-studio-setup.md.

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Trae

Trae supports MCP servers via its settings panel.

Settings → MCP → Add Server:

Field	Value
Name	zra-mcp
Transport	stdio
Command	Full path to .venv/bin/python (or .venv\Scripts\python.exe on Windows)
Arguments	-m project_a_mcp.server
Working Directory	Full path to the project root

Or add to your Trae MCP configuration file (.trae/mcp.json in your workspace or global config):

{
  "mcpServers": {
    "zra-mcp": {
      "command": "/Users/you/zotero-research-agent/.venv/bin/python",
      "args": ["-m", "project_a_mcp.server"],
      "cwd": "/Users/you/zotero-research-agent"
    }
  }
}

Restart Trae after configuration. MCP tools become available in AI chat (Agent mode).

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OpenAI Codex CLI

Codex CLI supports MCP servers. Add to your ~/.codex/config.json (or project-level .codex/config.json):

{
  "mcpServers": {
    "zra-mcp": {
      "command": "/Users/you/zotero-research-agent/.venv/bin/python",
      "args": ["-m", "project_a_mcp.server"],
      "cwd": "/Users/you/zotero-research-agent"
    }
  }
}

Then run Codex normally — it will discover and use the tools automatically:

codex "Find papers about urban accessibility in my Zotero library"

---

Other MCP Clients

Any client that supports the MCP stdio transport can connect. The universal config is:

Parameter	Value
Transport	stdio
Command	<project>/.venv/bin/python
Arguments	["-m", "project_a_mcp.server"]
Working directory	<project>
Environment	Reads from <project>/.env automatically

---

Example Workflows

Research Discovery

User: Find papers about 15-minute cities published after 2020
  → search_papers (local library)

User: Search online for recent studies on urban green infrastructure
  → search_online_literature (OpenAlex + CrossRef + S2)

User: I'm reading this paper [title, keywords]. Find me related literature.
  → find_related_literature (5 parallel strategies, verified results)

User: Show me who cites this paper and what it references
  → expand_citation_network (forward + backward citations)

Reading & Analysis

User: What does this paper say about the research methodology?
  → get_paper_content (semantic search within paper)

User: Summarize these 5 papers into a literature review about "method evolution"
  → generate_review_note → AI synthesizes thematic review with citations

User: My thesis is "public services are unevenly distributed" — find evidence
  → find_arguments (returns supporting + opposing passages with stance labels)

User: What should I read next?
  → recommend_papers (based on your annotation activity)

Writing & Citing

User: I'm writing: "Walkability is a key indicator of urban quality..." — suggest citations
  → suggest_citations (matches your draft to library evidence)

User: Export BibTeX for the top 3 results
  → export_bibliography

User: Add this paper: 10.1016/j.cities.2025.105902
  → add_paper (preview → confirm → auto-downloads OA PDF)

Library Organization

User: Analyze these papers and suggest tags
  → suggest_tags (methodology/domain/data classification, suggest-only)

User: Tag these papers as "core reading"
  → edit_tags (preview → confirm)

User: Which papers have I actually read? Which are unread?
  → reading_status (heuristic: annotations, notes, PDF open history)

Write safety: all destructive operations (add paper, notes, tags, merge duplicates) always preview first. The assistant asks for explicit confirmation before executing.

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MCP Tools (29)

Category	Tools
Discover	search_papers, search_online_literature, search_cnki_literature, find_related_literature, expand_citation_network, cnki_paper_detail, cnki_navigate_pages, find_similar_papers, browse_library, find_duplicates, merge_duplicates
Read	get_paper, get_paper_content, search_annotations, create_annotation
Write	suggest_citations, export_bibliography, add_paper, cnki_add_to_zotero
Manage	add_note, edit_tags, manage_collections
Insight	reading_status, recommend_papers, generate_review_note, generate_reading_note, suggest_tags, find_arguments
Admin	sync_index

Expand tool details

Discover

• search_papers — Primary search in your local library. Hybrid keyword + semantic. Use query="" with year_from / tags for filter-only listing.
• search_online_literature — Online discovery (English/international: OpenAlex, CrossRef, Semantic Scholar). Supports fields_of_study for discipline filtering. Default for online search unless user explicitly requests Chinese literature.
• search_cnki_literature — CNKI Chinese journal search (optional module, disabled by default). Only triggered when user explicitly requests Chinese papers / 中文文献 / CNKI. Returns journal-level tags (CSSCI, PKU Core, etc.).
• find_related_literature — Multi-strategy related paper search. Supports Corpus-First mode (reference_dois parameter), keyword search, citation network expansion, and Semantic Scholar recommendations — all in parallel. Provide a paper's metadata → get deduplicated, Three-Index-Verified results in one call.
• expand_citation_network — Find papers via citation relationships (forward & backward citations via OpenAlex). Accepts multiple DOIs for multi-seed expansion.
• cnki_paper_detail — Full metadata (abstract, keywords, DOI, affiliations) from a CNKI paper page.
• cnki_navigate_pages — Pagination & re-sorting for CNKI results. Used proactively when user needs many papers or deeper search.
• find_similar_papers — Similar papers to a known item (by item_key).
• browse_library — Collections, tags, recent items.
• find_duplicates / merge_duplicates — Detect and merge duplicates (dry-run by default).

Read

• get_paper — Metadata + abstract.
• get_paper_content — Modes: semantic query, page range, fulltext, outline; optional annotations overlay.
• search_annotations — Search highlights/comments across all papers.
• create_annotation — Highlight text on a PDF (dry-run by default).

Write & Manage

• suggest_citations — Match your draft text to library evidence.
• export_bibliography — BibTeX or formatted citations.
• add_paper — Import by DOI / arXiv / ISBN / BibTeX / URL (dry-run by default).
• cnki_add_to_zotero — Import CNKI papers directly (no DOI needed). Uses CNKI export API + Zotero Connector.
• add_note, edit_tags, manage_collections — Library organization (dry-run by default).

Insight

• reading_status — Analyze reading progress. Classifies papers as deep_read (≥3 annotations or notes), browsed (PDF opened recently in Zotero reader), or unread. Filter by scope.
• recommend_papers — Personalized recommendations. Identifies your most-engaged papers, finds related literature via OpenAlex + S2, deduplicates, and excludes already-in-library papers.
• generate_review_note — Extract evidence from multiple papers for literature review. Provide item keys + optional focus topic → returns passages with inline citations (Author, Year, p.X) ready for AI synthesis.
• generate_reading_note — Structured reading note for ONE paper. Auto-extracts research question, methodology, data, findings, limitations, and contribution from the PDF. Produces a template the AI refines into a concise note.
• suggest_tags — Analyze paper metadata to suggest methodology, domain, and data-type tags. Suggest-only — never auto-applies; user confirms via edit_tags.
• find_arguments — Given a claim/thesis, find supporting and opposing evidence from your library. Classifies passages by stance (support/oppose/neutral) with citations. For writing Discussion sections.

Admin

• sync_index — Incremental vector index sync. Also runs automatically on MCP startup.

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Configuration

Copy .env.example to .env and adjust:

Variable	Default	Description
ZOTERO_LOCAL	true	Read from local Zotero API (fast)
ZOTERO_API_KEY	—	Required for write operations (hybrid mode)
ZOTERO_LIBRARY_ID	0	Your Zotero user ID
EMBEDDING_MODEL	BAAI/bge-m3	Sentence-transformer for semantic search
RERANKER_MODEL	cross-encoder/ms-marco-MiniLM-L-6-v2	Reranker (none to disable)
CHROMA_PERSIST_DIR	.chroma_db	Local vector database path
ZRA_AUTO_SYNC	true	Auto incremental sync on MCP startup
SEMANTIC_SCHOLAR_API_KEY	—	Optional; higher rate limits for online search
OPENALEX_MAILTO	—	Optional; polite pool for OpenAlex API
UNPAYWALL_EMAIL	—	Optional; Unpaywall OA PDF lookup
CORE_API_KEY	—	Optional; CORE repository full-text
CNKI_ENABLED	false	Enable CNKI browser search (see below)
CNKI_CDP_URL	—	Chrome remote debugging URL

All data stays on your machine: Zotero library, .chroma_db/, and HuggingFace model cache (~/.cache/huggingface/).

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CNKI Setup (Optional)

CNKI (China National Knowledge Infrastructure) is disabled by default. It is only needed for searching Chinese-language journal papers. When you first ask the AI for Chinese literature (e.g., "search CNKI for…" or "检索中文文献"), it will prompt you to complete the setup below.

CNKI has no public API. This project uses Playwright to connect to your logged-in Chrome browser via CDP (Chrome DevTools Protocol), following the same approach as cookjohn/cnki-skills.

Step 1: Install optional dependencies

uv pip install -e ".[cnki]"
playwright install chromium

Step 2: Start Chrome with remote debugging

# macOS
/Applications/Google\ Chrome.app/Contents/MacOS/Google\ Chrome --remote-debugging-port=9222

# Windows
"C:\Program Files\Google\Chrome\Application\chrome.exe" --remote-debugging-port=9222

# Linux
google-chrome --remote-debugging-port=9222

Step 3: Log in to CNKI

Open https://www.cnki.net/ in that Chrome window and log in (typically requires institutional VPN or campus network).

Step 4: Enable in .env

CNKI_ENABLED=true
CNKI_CDP_URL=http://127.0.0.1:9222

Step 5: Restart the MCP server

Reopen a chat window or restart your MCP client.

Verify

Ask the AI: "Search CNKI for highly-cited papers on geodetector since 2020"

If results appear (with title, authors, journal, citations, and journal level tags like CSSCI/PKU Core), the setup is working.

How it works

1. search_cnki_literature or find_related_literature(scope="cnki") → returns hits with export_id and journal_level
2. You select papers → AI calls cnki_add_to_zotero(export_ids=[...]) → papers appear in Zotero
3. No DOI lookup needed; metadata is fetched from CNKI's internal export API

Notes

• Trigger: CNKI tools are only called when you explicitly mention Chinese literature, CNKI, 知网, 核心期刊, CSSCI, etc. Regular online search uses OpenAlex/CrossRef/S2.
• Captcha: If a Tencent slider captcha appears, solve it in the Chrome window and retry.
• Zotero import: Requires Zotero desktop running (uses localhost:23119 Connector API).
• Compliance: Requires legitimate institutional CNKI access.
• Before each session: Ensure the Chrome window from Step 2 is still running and the CNKI login is active.

Known Issues & Limitations

⚠️ The CNKI module is currently unstable and disabled by default. It relies on browser automation which is inherently fragile. Known issues include:

Issue	Cause	Workaround
Timeout on search	CNKI pages load slowly; anti-bot throttling	Simplify your query (fewer characters); retry after a few seconds
Chrome connection refused	Chrome was not started with --remote-debugging-port, or an existing session conflicted	Close ALL Chrome windows, then restart with --remote-debugging-port=9222 --user-data-dir="/tmp/chrome-debug-profile"
Stale login session	CNKI sessions expire after ~30 min of inactivity	Re-login in the Chrome window before retrying
Consecutive timeouts	Rate limiting by CNKI (>3 queries in quick succession)	The tool auto-aborts after 2 consecutive timeouts; wait 30s and retry
Export to Zotero fails	Zotero desktop not running or Connector API port changed	Ensure Zotero is running; verify http://localhost:23119/api/ responds
incorrect profile type errors in Chrome log	Normal Chrome warning when using a temporary --user-data-dir	Harmless — does not affect functionality

If CNKI consistently fails, fall back to the English-language online search (search_online_literature / find_related_literature) which is stable and does not require browser automation.

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Updating

cd ~/zotero-research-agent       # or your clone path
git pull
uv pip install -e .              # if dependencies changed

If using CNKI:

uv pip install -e ".[cnki]"
playwright install chromium

Restart your MCP client to reload the server.

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Troubleshooting

Problem	Fix
Connection refused / no results	Ensure Zotero desktop is running and local API is enabled
New papers not found	Say "sync my index" or restart MCP (auto-sync on startup)
Write operations fail	Set ZOTERO_API_KEY + ZOTERO_LIBRARY_ID in .env
Slow first start	Embedding model download (~2.3 GB); use HF_ENDPOINT=https://hf-mirror.com
Windows: script blocked	Set-ExecutionPolicy -Scope CurrentUser RemoteSigned in PowerShell
MCP tools not called	Use a model with function calling; enable MCP/tools in client settings
AI executes writes without asking	Add to system prompt: "Always wait for explicit confirmation before executing writes"
CNKI: "search is disabled"	Complete the CNKI Setup steps
CNKI: captcha	Solve the slider in the Chrome window, then retry the search

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Architecture

research_core/          # Shared library — Zotero client, RAG pipeline, search adapters, tools
project_a_mcp/          # MCP server entry point (stdio transport)
project_b_agent/        # Full-stack agent scaffold (planned)
scripts/                # CLI utilities (index_library.py, etc.)
tests/                  # Unit + integration tests
docs/                   # Detailed setup guides

Each tool maps to one user intent — discovery tools return item_key, read/write tools consume it.

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Development

uv pip install -e ".[dev]"
pytest tests/ -v
ruff check .
ruff format .

Run CNKI integration tests (requires active CNKI session):

CNKI_ENABLED=true CNKI_CDP_URL=http://127.0.0.1:9222 pytest tests/mcp/test_cnki.py -v

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Acknowledgments

This project was inspired by and built upon ideas from:

• zotero-mcp — Pioneering work on connecting Zotero with AI assistants via MCP.
• cnki-skills — Elegant approach to CNKI browser automation via Chrome DevTools Protocol.
• academic-research-skills — Inspiration for the Corpus-First search strategy and structured anti-hallucination patterns ([MATERIAL GAP] tagging).
• nature-skills — Inspiration for the Three-Index Verification approach (cross-checking citations against multiple bibliographic databases).

Thank you to the authors of these projects for sharing their work with the community.

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License

MIT