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fastmcp-me

ANSES Ciqual MCP Server

by fastmcp-me
OverviewSchemaSourceRelated ServersDiscussionsAdmin
Python
Local

Server Configuration

Describes the environment variables required to run the server.

NameRequiredDescriptionDefault

No arguments

Instructions

Guidance the server publishes about itself, which clients place ahead of the tool catalog so the model reads it before choosing anything.

This server publishes no instructions, or was last inspected before Glama recorded them.

Capabilities

Features and capabilities supported by this server

Protocol revision2025-11-25

CapabilityDetails
tools
{
  "listChanged": true
}
logging
{}
prompts
{
  "listChanged": false
}
resources
{
  "subscribe": false,
  "listChanged": false
}
extensions
{
  "io.modelcontextprotocol/ui": {}
}
experimental
{}

Tools

Functions exposed to the LLM to take actions

NameDescription
queryA

Execute SQL query on ANSES Ciqual French food composition database.

āš ļø EFFICIENCY: Follow this 2-step workflow to minimize queries!

STEP 1 - SEARCH (one query): SELECT alim_code, alim_nom_fr FROM foods_fts WHERE foods_fts MATCH 'steak OR boeuf'; Note: FTS uses OR between words. For "steak sauce poivre", search "steak" first.

STEP 2 - GET ALL NUTRIENTS (one query with JOIN): SELECT f.alim_nom_fr, n.const_nom_fr, 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_code = ;

šŸ›‘ STOP after finding a matching food! Don't keep searching with different terms.

COMPOUND DISHES (steak + sauce):

  • CIQUAL has individual ingredients, not full recipes

  • Search each component: "steak" then "sauce poivre"

  • Sum the calories (typical portions: meat 150g, sauce 30g)

QUICK CALORIE LOOKUP (const_code 328 = kcal/100g): SELECT f.alim_nom_fr, c.teneur as kcal_100g FROM foods f JOIN composition c ON f.alim_code = c.alim_code WHERE f.alim_code = AND c.const_code = 328;

KEY NUTRIENT CODES: Energy: 328 (kcal), 327 (kJ) Macros: 25000 (protein), 31000 (carbs), 40000 (fat), 34100 (fiber), 32000 (sugars) Minerals: 10110 (sodium), 10200 (calcium), 10260 (iron), 10190 (potassium), 10120 (magnesium) Vitamins: 55100 (vit C), 52100 (vit D), 56600 (vit B12), 53100 (vit E), 56700 (folates)

SCHEMA:

  • foods: alim_code (PK), alim_nom_fr, alim_nom_eng, alim_grp_code

  • nutrients: const_code (PK), const_nom_fr, const_nom_eng, unit

  • composition: alim_code, const_code, teneur (value per 100g), code_confiance

  • food_groups: grp_code, grp_nom_fr, grp_nom_eng

  • foods_fts: FTS5 virtual table for full-text search (alim_code, alim_nom_fr, alim_nom_eng)

Prompts

Interactive templates invoked by user choice

NameDescription

No prompts

Resources

Contextual data attached and managed by the client

NameDescription

No resources

Deploy Server
One endpoint for every MCP toolRayrun runs every MCP server behind one URL and holds the credentials. Each call is decided per client, tool, and argument, then recorded.ray.runAd

TDQS

A4.6/5.0

Scored across 1 tool

Disambiguation5/5

With only one tool, there is no possibility of confusion between tools. The tool's purpose is clearly stated as executing SQL queries on the food composition database.

Naming Consistency5/5

The sole tool is named 'query', a simple and unambiguous name. While there is no pattern to compare, the lack of multiple tools means there is no inconsistency in naming conventions.

Tool Count3/5

The server exposes only one tool, which feels thin for the broad scope of a food composition database. Although the tool is powerful and can execute arbitrary SQL, a single tool places a heavy burden on the agent and lacks dedicated conveniences.

Completeness5/5

The generic SQL query tool can access all tables, perform joins, filters, and full-text searches, covering every potential query needed. The provided schema, examples, and nutrient codes ensure agents have enough information to retrieve any data, making the surface functionally complete.

Maintenance

ActivityInactive
ResponsivenessNo issues

Resources