Autocomplete Open Food Facts taxonomy terms like categories, brands, countries, ingredients, additives, allergens, or labels from a query to get accurate suggestions.
Autocomplete an Open Food Facts taxonomy.
| Name | Required | Description | Default |
|---|---|---|---|
| limit | No | ||
| query | Yes | ||
| language | No | ||
| taxonomy | Yes |
| Name | Required | Description | Default |
|---|---|---|---|
| data | Yes | ||
| source | Yes | ||
| warnings | Yes | ||
| cache_hit | Yes | ||
| retrieved_at | Yes | ||
| missing_fields | Yes |
Changes observed during successful MCP inspections.
v0.1.0Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
Annotations already declare readOnlyHint, idempotentHint, and destructiveHint=false, so the safety profile is covered. The description adds nothing beyond the verb: no result format, no behavior for unmatched queries, no note on the limit default or language fallback, so it earns little credit even against the lower annotated bar.
Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.
Is the description appropriately sized, front-loaded, and free of redundancy?
A single front-loaded sentence with no waste, but it is terse to the point of under-specification for a four-parameter tool. Brevity here reflects omission rather than efficient editing.
Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.
Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?
An output schema exists, so return values need not be explained, but with 0% schema coverage and no usage guidance the definition is not complete enough to call the tool confidently. Language handling and the meaning of the limit cap are left entirely to inference.
Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.
Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?
Schema description coverage is 0%, so all four parameters (query, taxonomy, language, limit) rely on names alone. The description hints at the query/taxonomy pairing but adds no meaning for language matching rules or limit semantics, leaving the coverage gap uncompensated.
Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.
Does the description clearly state what the tool does and how it differs from similar tools?
States a specific verb (autocomplete) and resource (Open Food Facts taxonomy), which is clear enough to distinguish it from get_product and compare_products. However, it does not clearly separate itself from search_text, which could also be read as a text-autocomplete tool, so it falls short of full sibling differentiation.
Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.
Does the description explain when to use this tool, when not to, or what alternatives exist?
There is no guidance on when to use this tool versus search_text or search_products, no mention of prerequisites, and no exclusions. The agent must infer the use case entirely from the one-line purpose.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.