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JagjeevanAK

OpenFoodFacts-mcp

by JagjeevanAK

getRandomAIQuestions

Need to verify food product data? Retrieve random AI-generated questions from Robotoff that require human confirmation. Filter by barcode, insight type, language, and count to contribute to the Open Food Facts community database.

Instructions

Get random AI-generated questions from Robotoff that need human verification - great for community contribution

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
barcodeNoFilter by product barcode
insightTypeNoType of question to retrieve
langNoLanguage for questionsen
countNoNumber of questions to return
Behavior3/5

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

No annotations provided, so description carries full burden. It mentions 'AI-generated' and 'need human verification' indicating the data source and purpose, but lacks details on read-only nature, authentication, rate limits, or mutation potential.

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?

Single, concise sentence with no fluff. It front-loads the purpose and use case. Could be marginally improved with structure, but efficient.

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

Completeness3/5

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

Sufficient for a simple random fetch tool with 4 optional parameters and no output schema. However, lacks hints about return format or pagination, which would enhance completeness.

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

Parameters3/5

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

Schema coverage is 100%, so the description adds no parameter-specific information beyond the schema. Baseline 3 is appropriate.

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

Purpose4/5

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

The description clearly states it retrieves random AI-generated questions from Robotoff for human verification, with a community contribution use case. It distinguishes itself from sibling 'getProductAIQuestions' by emphasizing 'random' and 'need human verification', though not explicitly named.

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?

Implies usage for community contribution but does not explicitly state when to use vs alternatives like getProductAIQuestions, or any prerequisites. No when-not or alternative guidance.

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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