Personalized live suggestions

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

The description explicitly states 'Never changes the basket' and 'read-only', which are key behavioral traits. It also mentions re-fetching current products and optional Google Maps evidence. With no annotations provided, this description covers essential behavioral aspects but lacks details on authentication or rate limits.

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 two sentences, front-loaded with the core purpose, and every word adds value. No redundancy or wasted text.

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 11 parameters and no output schema, the description covers the main behavior but does not explain the structure of the returned choices (e.g., whether they include venue names, product details, pricing). This omission means the agent lacks full context on what the tool returns.

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 coverage is 100%, so baseline is 3. The description adds little beyond the schema; it mentions 'repeat, explore, or balanced food intent' which maps to the 'mode' parameter, but does not elaborate on other parameters. The schema itself provides adequate descriptions.

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 converts structured food intent (repeat/explore/balanced) into 3-5 read-only live choices, using venue history and re-fetching products. It distinguishes itself from siblings by focusing on personalized suggestions based on intent, not browsing or analysis.

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 when a food intent is known (repeat/explore/balanced), but it does not explicitly compare to sibling tools like glovo_browse_stores, glovo_search_store_items, or glovo_analyze_order_history, leaving the agent without clear guidance on when to choose this tool over alternatives.

Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.