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AiAgentKarl

Agentic Product Protocol MCP Server

convert_feed

Convert product feed URLs from JSON, CSV, or Open Food Facts into a standardized, agent-friendly schema for AI consumption.

Instructions

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)

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
feed_urlYes
formatNoopenfoodfacts

Output Schema

TableJSON Schema
NameRequiredDescriptionDefault

No arguments

Behavior2/5

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

No annotations are provided, so the description must fully disclose behavior. It states the output is a 'standardized format' but does not mention whether the operation is read-only, any side effects, or potential errors. Performance, rate limits, or state changes are not addressed.

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 (three sentences and a bulleted Args list), front-loaded with the key purpose, and every sentence adds value. No wasted words.

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 that an output schema exists, the description does not need to explain return values. It covers input parameters and the conversion purpose. It is sufficient for a conversion tool, though it could mention possible errors or limitations.

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?

The input schema has 0% description coverage in the schema itself, but the description compensates by explaining the feed_url as 'URL to the product feed' and format with its possible values and default, adding meaning beyond the bare schema properties.

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's purpose: converting a product feed URL into a normalized schema. It specifies the verb 'convert', the resource 'product feed URL', and the outcome 'agent-friendly normalized schema', and distinguishes from siblings like generate_product_schema by focusing on external feeds.

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 mentions supported formats (JSON, CSV, Open Food Facts) but does not explicitly state when to use this tool versus alternatives like generate_product_schema. The guidance is implicit, lacking when-not-to-use or exclusion criteria.

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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