ANSES Ciqual MCP Server

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An MCP (Model Context Protocol) server providing SQL access to the ANSES Ciqual French food composition database. Query nutritional data for over 3,000 foods with full-text search support.

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Features

• 🍎 Comprehensive Database: Access nutritional data for 3,185+ French foods
• 🔍 SQL Interface: Query using standard SQL with full flexibility
• 🌍 Bilingual Support: French and English food names
• 🔤 Fuzzy Search: Built-in full-text search with typo tolerance
• 📊 60+ Nutrients: Detailed composition including vitamins, minerals, macros, and more
• 🔄 Auto-Updates: Automatically refreshes data yearly from ANSES (checks on startup)
• 🔒 Read-Only: Safe queries with no risk of data modification
• 💾 Lightweight: ~10MB SQLite database with efficient indexing

Installation

Via pip

pip install ciqual-mcp

Via uvx (recommended)

uvx ciqual-mcp

From source

git clone https://github.com/zzgael/ciqual-mcp.git
cd ciqual-mcp
pip install -e .

MCP Client Configuration

Claude Desktop

Add to your Claude Desktop configuration:

macOS: ~/Library/Application Support/Claude/claude_desktop_config.json
Windows: %APPDATA%/Claude/claude_desktop_config.json
Linux: ~/.config/Claude/claude_desktop_config.json

{
  "mcpServers": {
    "ciqual": {
      "command": "uvx",
      "args": ["ciqual-mcp"]
    }
  }
}

Zed

Add to your Zed settings:

{
  "assistant": {
    "version": "2",
    "mcp": {
      "servers": {
        "ciqual": {
          "command": "uvx",
          "args": ["ciqual-mcp"]
        }
      }
    }
  }
}

Cline (VSCode Extension)

Add to your VSCode settings (settings.json):

{
  "cline.mcpServers": {
    "ciqual": {
      "command": "uvx",
      "args": ["ciqual-mcp"]
    }
  }
}

Continue.dev

Add to your Continue config (~/.continue/config.json):

{
  "mcpServers": [
    {
      "name": "ciqual",
      "command": "uvx",
      "args": ["ciqual-mcp"]
    }
  ]
}

Usage

As an MCP Server

The server implements the Model Context Protocol and exposes a single query function:

# Start the server standalone (for testing)
ciqual-mcp

Direct Python Usage

from ciqual_mcp.data_loader import initialize_database

# Initialize/update the database
initialize_database()

# Then use SQLite directly
import sqlite3
conn = sqlite3.connect("~/.ciqual/ciqual.db")
cursor = conn.execute("SELECT * FROM foods WHERE alim_nom_eng LIKE '%apple%'")

Database Schema

Tables

foods - Food items

• alim_code (INTEGER, PK): Unique food identifier
• alim_nom_fr (TEXT): French name
• alim_nom_eng (TEXT): English name
• alim_grp_code (TEXT): Food group code

nutrients - Nutrient definitions

• const_code (INTEGER, PK): Unique nutrient identifier
• const_nom_fr (TEXT): French name
• const_nom_eng (TEXT): English name
• unit (TEXT): Measurement unit (g/100g, mg/100g, etc.)

composition - Nutritional values

• alim_code (INTEGER): Food identifier
• const_code (INTEGER): Nutrient identifier
• teneur (REAL): Value per 100g
• code_confiance (TEXT): Confidence level (A/B/C/D)

foods_fts - Full-text search

Virtual table for fuzzy matching with French/English names

Common Nutrient Codes

Category	Code	Nutrient	Unit
Energy	327	Energy	kJ/100g
	328	Energy	kcal/100g
Macros	25000	Protein	g/100g
	31000	Carbohydrates	g/100g
	40000	Fat	g/100g
	34100	Fiber	g/100g
	32000	Sugars	g/100g
Minerals	10110	Sodium	mg/100g
	10200	Calcium	mg/100g
	10260	Iron	mg/100g
	10190	Potassium	mg/100g
Vitamins	55400	Vitamin C	mg/100g
	56400	Vitamin D	µg/100g
	51330	Vitamin B12	µg/100g

Example Queries

Basic Search

-- Find foods by name
SELECT * FROM foods WHERE alim_nom_eng LIKE '%orange%';

-- Fuzzy search (handles typos)
SELECT * FROM foods_fts WHERE foods_fts MATCH 'orang*';

Nutritional Queries

-- Get vitamin C content for oranges
SELECT f.alim_nom_eng, c.teneur as vitamin_c_mg
FROM foods f
JOIN composition c ON f.alim_code = c.alim_code
WHERE f.alim_nom_eng LIKE '%orange%' 
  AND c.const_code = 55400;

-- Find foods highest in protein
SELECT f.alim_nom_eng, c.teneur as protein_g
FROM foods f
JOIN composition c ON f.alim_code = c.alim_code
WHERE c.const_code = 25000
ORDER BY c.teneur DESC
LIMIT 10;

-- Compare macros for different foods
SELECT 
    f.alim_nom_eng as food,
    MAX(CASE WHEN c.const_code = 25000 THEN c.teneur END) as protein_g,
    MAX(CASE WHEN c.const_code = 31000 THEN c.teneur END) as carbs_g,
    MAX(CASE WHEN c.const_code = 40000 THEN c.teneur END) as fat_g,
    MAX(CASE WHEN c.const_code = 328 THEN c.teneur END) as calories_kcal
FROM foods f
JOIN composition c ON f.alim_code = c.alim_code
WHERE f.alim_nom_eng IN ('Apple, raw', 'Banana, raw', 'Orange, raw')
  AND c.const_code IN (25000, 31000, 40000, 328)
GROUP BY f.alim_code, f.alim_nom_eng;

Dietary Restrictions

-- Find low-sodium foods (<100mg/100g)
SELECT f.alim_nom_eng, c.teneur as sodium_mg
FROM foods f
JOIN composition c ON f.alim_code = c.alim_code
WHERE c.const_code = 10110 
  AND c.teneur < 100
ORDER BY c.teneur ASC;

-- High-fiber foods (>5g/100g)
SELECT f.alim_nom_eng, c.teneur as fiber_g
FROM foods f
JOIN composition c ON f.alim_code = c.alim_code
WHERE c.const_code = 34100 
  AND c.teneur > 5
ORDER BY c.teneur DESC;

Data Source

Data is sourced from the official ANSES Ciqual database:

• Website: https://ciqual.anses.fr/
• Data portal: https://www.data.gouv.fr/fr/datasets/table-de-composition-nutritionnelle-des-aliments-ciqual/

The database is automatically updated yearly when the server starts (data hasn't changed since 2020, so yearly updates are sufficient).

Requirements

• Python 3.9 or higher
• 50MB free disk space (for database)
• Internet connection (for initial data download)

License

MIT License - See LICENSE file for details

Contributing

Contributions are welcome! Please feel free to submit a Pull Request.

Development

Running Tests

# Install development dependencies
pip install -e .
pip install pytest pytest-asyncio

# Run unit tests
python -m pytest tests/test_server.py -v

# Run functional tests (requires database)
python -m pytest tests/test_functional.py -v

Troubleshooting

Database not initializing

• Check internet connection
• Ensure write permissions to ~/.ciqual/ directory
• Try manual initialization: python -m ciqual_mcp.data_loader

XML parsing errors

• The tool handles malformed XML automatically with recovery mode
• If issues persist, delete ~/.ciqual/ciqual.db and restart

Credits

Developed by Gael Debost as part of GPT Workbench, a multi-LLM interface for medical research developed by Ideagency.

Data provided by ANSES (Agence nationale de sécurité sanitaire de l'alimentation, de l'environnement et du travail).

Citation

If you use this tool in your research, please cite:

@software{ciqual_mcp,
  title = {ANSES Ciqual MCP Server},
  author = {Gael Debost},
  year = {2025},
  url = {https://github.com/zzgael/ciqual-mcp}
}