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instacart_departments

Read-only

Instacart store department/category taxonomy (Produce, Dairy & Eggs, Bakery, ...) two levels deep, metadata only.

Input Schema

NameRequiredDescriptionDefault
shop_idYesStore's opaque shop id, from GET /instacart/stores
store_slugYesStore's retailer slug, from GET /instacart/stores
postal_codeYesPostal code to localize the taxonomy for

Output Schema

NameRequiredDescriptionDefault
dataYesThe tool result payload (shape varies per tool; see each tool's docs resource).

Schema Changelog

Changes observed during successful MCP inspections.

  1. Changed3 schema fields changed
    • addedInput schema / properties / postal_code / description
      Added value: +"Postal code to localize the taxonomy for"
    • addedInput schema / properties / shop_id / description
      Added value: +"Store's opaque shop id, from GET /instacart/stores"
    • addedInput schema / properties / store_slug / description
      Added value: +"Store's retailer slug, from GET /instacart/stores"
  2. First observed

TDQS

A3.6/5.0
Behavior4/5

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

Annotations already declare readOnlyHint=true and openWorldHint=true, covering the safety profile. The description adds useful behavioral context beyond annotations: the taxonomy is 'two levels deep' and returns 'metadata only,' clarifying that no product data is included. It does not discuss auth or rate limits, but for a read-only taxonomy this is a meaningful addition.

Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.

Conciseness5/5

Is the description appropriately sized, front-loaded, and free of redundancy?

The description is a single, front-loaded sentence with zero waste. The parenthetical examples and the 'two levels deep, metadata only' clause each add immediate clarity without padding.

Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.

Completeness4/5

Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?

With an output schema present, the description need not explain return values. It states the taxonomy scope and metadata-only nature, which is sufficient for this simple read tool. However, it does not mention that shop_id and store_slug come from instacart_stores, leaving that context entirely to the schema.

Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.

Parameters3/5

Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?

Schema description coverage is 100%, with all three parameters (shop_id, store_slug, postal_code) documented in the schema. The description adds no parameter-level meaning beyond what the schema already provides, so the baseline of 3 applies.

Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.

Purpose4/5

Does the description clearly state what the tool does and how it differs from similar tools?

The description names the specific resource (Instacart store department/category taxonomy), gives examples, and scopes it as 'two levels deep, metadata only.' It clearly distinguishes the tool from product-search siblings by calling out taxonomy and metadata. It lacks an explicit verb and does not directly name an alternative sibling, so a 4 rather than a 5.

Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.

Usage Guidelines2/5

Does the description explain when to use this tool, when not to, or what alternatives exist?

The description provides no when-to-use guidance, no prerequisites, and no alternatives. It does not say to use this instead of instacart_stores or instacart_item when browsing a store's navigation. The agent must infer usage entirely.

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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