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AisleNode Local Commerce (Meta Muse & Claude MCP)

create_order

Reserve in-stock items at a local physical store and generate a zero-PII, non-custodial checkout URL. The shopper completes payment and delivery details directly on the verified merchant checkout page.

Input Schema

NameRequiredDescriptionDefault
itemsYesArray of products and quantities to reserve
store_idYesUUID of the verified physical store
agent_sourceNoIdentifier of the calling AI agent (e.g. "meta_muse", "claude_desktop")meta_muse

Schema Changelog

Changes observed during successful MCP inspections.

  1. First observed

TDQS

A4/5.0
Behavior3/5

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

With no annotations, the description carries the full behavioral disclosure burden. It helpfully reveals that the checkout URL is zero-PII and non-custodial, and that payment and delivery details are handled on the merchant page. However, it omits important operational details such as what happens if items are out of stock, reservation expiration, idempotency, or authentication requirements.

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 two sentences, front-loaded with the core action and result, with no wasted words. Every sentence earns its place by clarifying the tool's purpose and the checkout flow.

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?

Given the absence of an output schema and annotations, the description does well to explain that a checkout URL is generated and that the shopper completes payment elsewhere. It covers the essential flow but could be slightly more complete by mentioning expected return format or error conditions.

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%, so all parameters (store_id, items, agent_source) are already documented in the schema. The description adds no parameter-specific meaning beyond what the schema provides, so the baseline score of 3 is appropriate.

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

Purpose5/5

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

The description states specific verbs and resources: 'Reserve in-stock items at a local physical store' and 'generate a zero-PII, non-custodial checkout URL.' This clearly distinguishes the tool from siblings like search_catalog (search), check_order_status (status check), and report_store (reporting). An agent can immediately understand the tool's function without opening the schema.

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

Usage Guidelines4/5

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

The description implies usage context ('Reserve in-stock items at a local physical store') and the flow (shopper completes payment on merchant page), but it does not explicitly state when to use this tool versus alternatives or any preconditions. However, the context is clear enough for an agent to infer appropriate use.

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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