Agentic Product Protocol MCP Server
Server Configuration
Describes the environment variables required to run the server.
| Name | Required | Description | Default |
|---|---|---|---|
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
| Capability | Details |
|---|---|
| tools | {
"listChanged": false
} |
| prompts | {
"listChanged": false
} |
| resources | {
"subscribe": false,
"listChanged": false
} |
| experimental | {} |
Tools
Functions exposed to the LLM to take actions
| Name | Description |
|---|---|
| search_productsA | Search products with agent-friendly structured results. Returns normalized product data including name, categories, nutrition scores, labels, and availability information. Uses Open Food Facts as demo data source. Args: query: Search term (e.g. "organic chocolate", "vegan protein") category: Optional category filter (e.g. "chocolates", "beverages") max_price: Optional maximum price filter (not available for OFF data) limit: Number of results to return (default 10, max 50) |
| get_product_detailsA | Get full structured product data by ID (barcode). Returns complete product information including nutrition facts, ingredients, allergens, certifications, and environmental scores. Args: product_id: Product barcode/EAN (e.g. "3017620422003" for Nutella) |
| compare_productsA | Side-by-side product comparison for AI agents. Compares multiple products across key dimensions: nutrition, labels, environmental impact, and ingredients. Args: product_ids: List of product barcodes to compare (2-5 products) |
| convert_feedA | Convert a product feed URL into agent-friendly normalized schema. Takes any product feed (JSON, CSV, Open Food Facts) and converts it into a standardized format that AI agents can easily consume. Args: feed_url: URL to the product feed (JSON or CSV) format: Feed format — "openfoodfacts" (OFF search URL), "json" (generic JSON), or "csv" (CSV file) |
| generate_product_schemaA | Generate a standardized agent-readable product listing. Takes raw product data and generates a structured schema following the Agentic Product Protocol concept — making products machine-readable for AI shopping agents. The output schema includes:
Args: product_data: Raw product data dict with fields like name, price, description, category, etc. |
| check_availabilityA | Check real-time product availability and pricing. Fetches current product data and returns availability status, store information, and last-updated timestamp. Note: Open Food Facts is a community database — availability reflects reported store data, not real-time inventory. Args: product_id: Product barcode/EAN to check |
Prompts
Interactive templates invoked by user choice
| Name | Description |
|---|---|
No prompts | |
Resources
Contextual data attached and managed by the client
| Name | Description |
|---|---|
No resources | |
TDQS
Scored across 6 tools
Each tool has a distinct purpose: real-time availability, comparison, feed conversion, schema generation, detailed product info, and search. No overlap or ambiguity.
All tool names follow a consistent verb_noun pattern using underscores. Examples: check_availability, compare_products, search_products. No mixed styles.
With 6 tools, the server is well-scoped for its purpose. Each tool covers a core functionality without excess or deficiency.
The tool set covers the full lifecycle of product information retrieval: search, details, comparison, availability, schema generation, and feed ingestion. No obvious gaps for the domain.