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Agentic Product Protocol MCP Server

Agentic Product Protocol MCP Server

by AiAgentKarl
OverviewSchemaSourceRelated ServersDiscussionsAdmin
Python
Remote

compare_products

Compare up to five products side by side across nutrition, labels, environmental impact, and ingredients using barcodes.

Instructions

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)

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_ids": "<product_ids>"
    },
    "name": "compare_products"
  }
}

Input Schema

NameRequiredDescriptionDefault
product_idsYes

Output Schema

NameRequiredDescriptionDefault

No arguments

Schema Changelog

Changes observed during successful MCP inspections.

  1. First observedv0.1.0

TDQS

A4.3/5.0
Behavior3/5

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

No annotations are provided, so the description must fully convey behavioral traits. It describes the comparison action and dimensions but omits details on error handling, authentication requirements, or whether the operation is read-only. It reveals the parameter constraint (2-5 products) but does not explain the return format, which is partly mitigated by the existence of an output schema.

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

Conciseness5/5

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

The description is extremely concise with no wasted words. It front-loads the core purpose, then lists key dimensions, and finally defines the argument. The structure is clear and easy to parse.

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

Completeness4/5

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

Given the existence of an output schema, the description need not detail return values. It covers the main functionality well, but could provide slightly more context about expected behavior when ids are invalid or not found. Overall, it is sufficiently complete for a simple comparison tool.

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

Parameters5/5

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

The input schema provides minimal information (array of strings) with 0% description coverage. The description compensates fully by specifying that product_ids are 'product barcodes' and constraining them to '2-5 products', adding significant meaning beyond the schema. For a single parameter, this is excellent.

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 performs 'side-by-side product comparison' and lists specific dimensions (nutrition, labels, environmental impact, ingredients). It distinguishes itself from sibling tools like get_product_details (single product) and search_products (search) by focusing on comparison.

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

Usage Guidelines4/5

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

The description implies usage when comparing multiple products across dimensions, providing clear context. However, it lacks explicit when-not-to-use statements or references to alternative sibling tools for specific use cases, so it is not a perfect 5.

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

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