Content
# 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.
<a href="https://glama.ai/mcp/servers/@zzgael/ciqual-mcp">
<img width="380" height="200" src="https://glama.ai/mcp/servers/@zzgael/ciqual-mcp/badge" alt="ANSES Ciqual Server MCP server" />
</a>
</div>
## 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
```bash
pip install ciqual-mcp
```
### Via uvx (recommended)
```bash
uvx ciqual-mcp
```
### From source
```bash
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`
```json
{
"mcpServers": {
"ciqual": {
"command": "uvx",
"args": ["ciqual-mcp"]
}
}
}
```
### Gemini CLI
Add to your Gemini CLI configuration file `~/.gemini/settings.json`:
```json
{
"mcpServers": {
"ciqual": {
"command": "uvx",
"args": ["ciqual-mcp"]
}
}
}
```
### Codex CLI
Add to your Codex CLI configuration file `~/.codex/config.toml`:
```toml
[mcp_servers.ciqual]
command = "uvx"
args = ["ciqual-mcp"]
```
## Usage
### As an MCP Server
The server implements the Model Context Protocol and exposes a single `query` function:
```bash
# Start the server standalone (for testing)
ciqual-mcp
```
### Direct Python Usage
```python
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%'")
```
## API Documentation
### MCP Function: `query`
The server exposes a single MCP function for executing SQL queries on the Ciqual database.
#### Function Signature
```python
async def query(sql: str) -> list[dict]
```
#### Parameters
- **`sql`** (string, required): The SQL query to execute on the database
- Must be a SELECT or WITH query (read-only access)
- Supports all standard SQLite SQL syntax
- Can use JOIN, GROUP BY, ORDER BY, etc.
- Supports full-text search via the `foods_fts` table
#### Returns
- **`list[dict]`**: Array of result rows, where each row is a dictionary with column names as keys
- Empty list if no results match the query
- Error dictionary with `"error"` key if query fails
#### Error Handling
The function returns an error dictionary in these cases:
- Database not initialized: `{"error": "Database not initialized..."}`
- Non-SELECT query attempted: `{"error": "Only SELECT queries are allowed for safety."}`
- SQL syntax error: `{"error": "SQL error: [details]"}`
- Table not found: `{"error": "Table not found. Available tables: foods, nutrients, composition, foods_fts, food_groups"}`
#### Example Usage in MCP Context
```json
{
"method": "query",
"params": {
"sql": "SELECT f.alim_nom_eng, n.const_nom_eng, c.teneur, n.unit FROM foods f JOIN composition c ON f.alim_code = c.alim_code JOIN nutrients n ON c.const_code = n.const_code WHERE f.alim_nom_eng LIKE '%apple%' AND n.const_code IN (328, 25000, 31000)"
}
}
```
#### Response Example
```json
[
{
"alim_nom_eng": "Apple, raw",
"const_nom_eng": "Energy",
"teneur": 52.0,
"unit": "kcal/100g"
},
{
"alim_nom_eng": "Apple, raw",
"const_nom_eng": "Protein",
"teneur": 0.3,
"unit": "g/100g"
}
]
```
## 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
```sql
-- 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
```sql
-- 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
```sql
-- 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
```bash
# 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](https://ideagency.fr).
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:
```bibtex
@software{ciqual_mcp,
title = {ANSES Ciqual MCP Server},
author = {Gael Debost},
year = {2025},
url = {https://github.com/zzgael/ciqual-mcp}
}
```
