Agentic Product Protocol MCP Server

generate_product_schema

Converts raw product data into a standardized, machine-readable schema for AI shopping agents, enabling search, comparison, and purchase actions.

Instructions

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:

Unique identification
Structured attributes (name, brand, category)
Pricing with currency
Availability status
Specifications and nutrition
Agent action hints (comparable, purchasable)

Args: product_data: Raw product data dict with fields like name, price, description, category, etc.

Tool call template

Replace each placeholder with a value matching the input schema. Only required parameters are included; add optional parameters as needed.

{
  "id": 1,
  "jsonrpc": "2.0",
  "method": "tools/call",
  "params": {
    "arguments": {
      "product_data": "<product_data>"
    },
    "name": "generate_product_schema"
  }
}

Input Schema

Name	Required	Description	Default
`product_data`	Yes

Output Schema

Name	Required	Description	Default
No arguments

Schema Changelog

Changes observed during successful MCP inspections.

First observedv0.1.0May 29, 2026

TDQS

A4.2/5.0

Behavior4/5

Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?

No annotations exist, so the description carries full burden. It discloses that the tool transforms raw data into a structured schema and lists output fields, implying no side effects. It is transparent enough for a simple generator.

Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.

Conciseness4/5

Is the description appropriately sized, front-loaded, and free of redundancy?

The description is well-structured with a clear one-liner, explanation, bullet list, and parameter detail. It is not overly verbose, though the bullet list could be slightly trimmed.

Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.

Completeness5/5

Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?

Given only one parameter, no annotations, and an output schema (referenced), the description fully explains the tool's operation, input requirements, and output structure, making it complete.

Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.

Parameters4/5

Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?

Schema coverage is 0%, but the description compensates by detailing the expected fields in 'product_data' (name, price, description, category). This adds meaningful context beyond the schema.

Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.

Purpose5/5

Does the description clearly state what the tool does and how it differs from similar tools?

The description clearly states the tool generates a standardized agent-readable product listing from raw data, specifying the verb and resource. It implicitly distinguishes from siblings like 'check_availability' and 'search_products' by focusing on schema generation.

Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.

Usage Guidelines3/5

Does the description explain when to use this tool, when not to, or what alternatives exist?

The description explains the tool's purpose but does not explicitly state when to use it versus alternatives or provide exclusions. It implies usage for creating machine-readable product schemas but lacks direct guidance.

Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.

Deploy Server