Search food products by name (Open Food Facts + USDA). Returns macros per 100g.
| Name | Required | Description | Default |
|---|---|---|---|
| q | Yes | ||
| locale | No | ||
| page_size | No |
| Name | Required | Description | Default |
|---|---|---|---|
| count | No | ||
| items | No |
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
Annotations already declare readOnlyHint, openWorldHint, and idempotentHint. The description adds value by disclosing data sources and the per-100g macro return format, which are not covered by annotations. It does not discuss pagination or locale behavior, but with annotations present, this is sufficient.
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?
The description is a single, well-structured sentence that concisely states the purpose, data sources, and return format without extraneous words.
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?
With a simple tool and an output schema present, the description covers the core function and return format but lacks usage guidance and parameter explanations. It is adequate for a straightforward search but leaves some gaps in context for the agent.
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 the description must compensate. It clarifies the 'q' parameter as a name search but provides no semantics for 'locale' or 'page_size'. The names are self-explanatory to some degree, but the description does not explicitly explain them, leaving ambiguity for the agent.
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?
The description clearly states 'Search food products by name' with a specific verb and resource, and identifies data sources (Open Food Facts + USDA) and the return format (macros per 100g). This distinguishes it from sibling tools like search_exercises and lookup_barcode.
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?
The description implies usage by name but does not explicitly state when to use this tool versus alternatives such as lookup_barcode for barcode queries. No exclusions or alternative recommendations are provided, leaving the agent to infer context from sibling names.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
Add one secure layer between your agents and this server.
Most tools have clearly distinct purposes (meals, workouts, routines, body metrics, etc.). A few pairs like get_progress and get_muscle_recovery overlap in data but differ in usage, and the AI tools are separated by input type. Overall, an agent can reliably select the right tool.
Tool names predominantly follow a verb_noun snake_case pattern (get_, log_, update_, delete_, search_, list_). The ai_* prefix is consistent but includes noun-like names (ai_meal_plan, ai_photo_macros) that deviate slightly from verb-first convention. Still predictable and readable.
With 36 tools, the server exceeds the typical 'heavy' threshold, but the scope is broad covering meals, workouts, routines, metrics, AI features, and data sync. Each tool serves a distinct capability, so the count is justified though on the higher end.
The server provides solid lifecycle coverage for core resources: meals (create/read/update/delete), workouts (log/get/delete, set updates), body metrics (get/log with profile upsert), routines (list/get/instantiate), and exercise lookup (search/resolve/list). Minor gaps exist for updating/deleting cardio and water entries, but they are not critical.