Fetch popular Instacart search terms across retailers serving a postal code, like the blank-search suggestions. Use instacart_search for a typed term or instacart_search_nearby for products.
| Name | Required | Description | Default |
|---|---|---|---|
| postal_code | Yes | US postal/ZIP code to search near |
| Name | Required | Description | Default |
|---|---|---|---|
| data | Yes | The tool result payload (shape varies per tool; see each tool's docs resource). |
Changes observed during successful MCP inspections.
Input schema / properties / postal_code / descriptionAdded value: +"US postal/ZIP code to search near"Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
Annotations already declare readOnlyHint and openWorldHint, so the safety profile is covered. The description adds real behavioral context by explaining this mirrors blank-search suggestions and is scoped to retailers serving the postal code — useful for knowing what data comes back — but does not mention freshness, ranking, or result 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?
Two compact sentences, zero filler, with the core purpose front-loaded and the disambiguation trailing. Every clause earns its place.
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 described, annotations cover the read-only/open-world profile, and the single parameter is documented. The description supplies the disambiguation and data nature, leaving nothing an agent needs to call it correctly.
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% with a single postal_code parameter fully documented in the schema. The description references the postal code only implicitly, so it adds no syntax or format detail beyond the structured field. Baseline 3 is appropriate.
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 (fetch) and resource (popular Instacart search terms), plus the geographic scope (across retailers serving a postal code). The 'blank-search suggestions' analogy makes the exact data type concrete and instantly distinguishes it from the typed-term and product siblings.
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?
Explicitly routes the agent: use instacart_search for a typed term and instacart_search_nearby for products, implying this tool is for discovering trends/autocomplete-style suggestions without a query. Both alternatives and the selecting condition are named.
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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