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Jatin-IITB

Open Food Facts MCP Server

by Jatin-IITB

getRandomAIQuestions

Retrieve random AI-generated questions from Robotoff that need human verification to improve food product data accuracy. Filter by barcode, insight type, language, or count.

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
Behavior2/5

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

No annotations are provided, so the description carries full burden. It states the tool retrieves questions needing verification, suggesting a read-only operation, but does not explicitly confirm non-destructive behavior, rate limits, or behavior when no questions are available. Minimal disclosure beyond the basic action.

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 a single sentence, making it very concise. It front-loads the core action effectively. It could be improved by structuring the use-case hint ('great for community contribution') more prominently, but overall it is 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?

The tool is simple (random retrieval) with optional parameters and no output schema. The description covers the basic purpose but lacks details on return format, error handling, and pagination. It is adequate for a minimal tool but not fully complete.

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% with descriptions for all four parameters (barcode, insightType, lang, count). The tool description adds no additional parameter information, so baseline score of 3 is appropriate. No extra semantics are provided.

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 the action 'Get random AI-generated questions from Robotoff that need human verification'. It specifies the resource (Robotoff questions) and the selection method (random). However, it does not explicitly differentiate from sibling tools like getProductAIQuestions, though the name implies a distinction.

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 phrase 'great for community contribution' implies a use case, but there is no explicit guidance on when to use this tool versus alternatives (e.g., getProductAIQuestions for specific products). No when-not-to-use or exclusion criteria are provided.

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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