Read a user food profile — household members, dietary constraints, allergens, medications, purchase history, and cross-AI agent memory. The profile URL (food.rootz.global/p/{hash}) is the persistent Layer 9 that any AI can read. Use this to personalize food recommendations.
| Name | Required | Description | Default |
|---|---|---|---|
| profile_hash | Yes | Profile hash from the URL (e.g., "8f3k9x2p") |
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
The description specifies the tool is a read operation ('Read') and notes the profile URL is persistent and accessible by any AI, indicating read-only behavior. With no annotations provided, this adequately covers the main behavioral traits.
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?
Three concise sentences: first lists contents, second gives URL format, third states purpose. No unnecessary words, and key information is front-loaded.
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?
Given the tool's simplicity (one parameter, no output schema), the description sufficiently covers what the tool does, its inputs, and intended use. The lack of output schema is partially compensated by listing the returned fields in the description.
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?
The schema already describes the profile_hash parameter (100% coverage). The description adds valuable context by explaining the URL format (food.rootz.global/p/{hash}) and referring to it as Layer 9, which enhances understanding beyond the schema.
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 the tool reads a user food profile, enumerates specific contents (household members, dietary constraints, etc.), and distinguishes it from sibling tools like food_commodity_prices or food_diet_filter.
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 explicitly advises using this tool to personalize food recommendations, providing clear context. However, it does not mention when to avoid using it or alternative tools.
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.
Each tool targets a specific aspect of food data: prices, nutrition, recalls, dietary filters, supply chain, etc. Overlaps are minimal and clearly differentiated by scope, such as full nutrition vs. ingredient lists.
All tools consistently use the 'food_' prefix followed by a descriptive snake_case term. While the stems vary between nouns and verbs, the pattern is uniform and predictable.
With 18 tools, the server comprehensively covers the grocery domain including prices, nutrition, recalls, dietary needs, supply chain, and more. Each tool serves a distinct purpose without being overwhelming.
The tool set is remarkably complete, covering search, detailed product info, price comparisons across supply chain, dietary constraints, household meal planning, recalls, receipts, and data source transparency. No critical gaps apparent.