Live demonstration of MCP server integration with Claude Code for epilepsy seizure prediction literature review:

• Full-text search on title, abstracts, and keywords across 167M papers (22ms response)

📄 Full demo documentation | 📊 Generated diagrams

Built for the LLM era - features that matter for AI research assistants:

Perfect for: RAG systems, research assistants, literature review automation.

pip install crossref-local

From source:

git clone https://github.com/ywatanabe1989/crossref-local
cd crossref-local && make install

Database setup (1.5 TB, ~2 weeks to build):

# 1. Download CrossRef data (~100GB compressed)
aria2c "https://academictorrents.com/details/..."

# 2. Build SQLite database (~days)
pip install dois2sqlite
dois2sqlite build /path/to/crossref-data ./data/crossref.db

# 3. Build FTS5 index (~60 hours) & citations table (~days)
make fts-build-screen
make citations-build-screen

from crossref_local import search, get, count

# Full-text search (22ms for 541 matches across 167M records)
results = search("hippocampal sharp wave ripples")
for work in results:
    print(f"{work.title} ({work.year})")

# Get by DOI
work = get("10.1126/science.aax0758")
print(work.citation())

# Count matches
n = count("machine learning")  # 477,922 matches

Async API:

from crossref_local import aio

async def main():
    counts = await aio.count_many(["CRISPR", "neural network", "climate"])
    results = await aio.search("machine learning")

crossref-local search "CRISPR genome editing" -n 5
crossref-local search-by-doi 10.1038/nature12373
crossref-local status  # Configuration and database stats

With abstracts (-a flag):

$ crossref-local search "RS-1 enhances CRISPR" -n 1 -a

Found 4 matches in 128.4ms

1. RS-1 enhances CRISPR/Cas9- and TALEN-mediated knock-in efficiency (2016)
   DOI: 10.1038/ncomms10548
   Journal: Nature Communications
   Abstract: Zinc-finger nuclease, transcription activator-like effector nuclease
   and CRISPR/Cas9 are becoming major tools for genome editing...

Start the FastAPI server:

crossref-local relay --host 0.0.0.0 --port 31291

Endpoints:

# Search works (FTS5)
curl "http://localhost:31291/works?q=CRISPR&limit=10"

# Get by DOI
curl "http://localhost:31291/works/10.1038/nature12373"

# Batch DOI lookup
curl -X POST "http://localhost:31291/works/batch" \
  -H "Content-Type: application/json" \
  -d '{"dois": ["10.1038/nature12373", "10.1126/science.aax0758"]}'

# Citation endpoints
curl "http://localhost:31291/citations/10.1038/nature12373/citing"
curl "http://localhost:31291/citations/10.1038/nature12373/cited"
curl "http://localhost:31291/citations/10.1038/nature12373/count"

# Collection endpoints
curl "http://localhost:31291/collections"
curl -X POST "http://localhost:31291/collections" \
  -H "Content-Type: application/json" \
  -d '{"name": "my_papers", "query": "CRISPR", "limit": 100}'
curl "http://localhost:31291/collections/my_papers/download?format=bibtex"

# Database info
curl "http://localhost:31291/info"

HTTP mode (connect to running server):

# On local machine (if server is remote)
ssh -L 31291:127.0.0.1:31291 your-server

# Python client
from crossref_local import configure_http
configure_http("http://localhost:31291")

# Or via CLI
crossref-local --http search "CRISPR"

Run as MCP (Model Context Protocol) server:

crossref-local mcp start

Local MCP client configuration:

{
  "mcpServers": {
    "crossref-local": {
      "command": "crossref-local",
      "args": ["mcp", "start"],
      "env": {
        "CROSSREF_LOCAL_DB": "/path/to/crossref.db"
      }
    }
  }
}

Remote MCP via HTTP (recommended):

# On server: start persistent MCP server
crossref-local mcp start -t http --host 0.0.0.0 --port 8082

{
  "mcpServers": {
    "crossref-remote": {
      "url": "http://your-server:8082/mcp"
    }
  }
}

Diagnose setup:

crossref-local mcp doctor        # Check dependencies and database
crossref-local mcp list-tools    # Show available MCP tools
crossref-local mcp installation  # Show client config examples

See docs/remote-deployment.md for systemd and Docker setup.

Available tools:

• search - Full-text search across 167M+ papers
• search_by_doi - Get paper by DOI
• enrich_dois - Add citation counts and references to DOIs
• status - Database statistics
• cache_* - Paper collection management

from crossref_local.impact_factor import ImpactFactorCalculator

with ImpactFactorCalculator() as calc:
    result = calc.calculate_impact_factor("Nature", target_year=2023)
    print(f"IF: {result['impact_factor']:.3f}")  # 54.067

from crossref_local import get_citing, get_cited, CitationNetwork

citing = get_citing("10.1038/nature12373")  # 1539 papers
cited = get_cited("10.1038/nature12373")

# Build visualization (like Connected Papers)
network = CitationNetwork("10.1038/nature12373", depth=2)
network.save_html("citation_network.html")  # requires: pip install crossref-local[viz]

Searching 167M records in milliseconds via FTS5.

openalex-local - Sister project with OpenAlex data:

When to use CrossRef: Real-time DOI updates, raw reference parsing, authoritative metadata. When to use OpenAlex: Semantic search, citation analysis, topic discovery.

from crossref_local import crossref_search, get_work

results = crossref_search("deep learning EEG", limit=10)
work = get_work("10.1038/nature12373")

crossref-local search "query"
crossref-local doi 10.1038/nature12373

crossref-local mcp start

Agent skill pages live under src/crossref_local/_skills/crossref-local/.