Choose which signed-in account to order with at a restaurant, e.g. to switch between work and personal. The account must already be signed in via login.
| Name | Required | Description | Default |
|---|---|---|---|
| label | Yes | Account label to make active, e.g. 'work'. | |
| clientId | No | clientId of the restaurant. Omit only when the server is configured for a single restaurant. |
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
With no annotations provided, the description carries the full behavioral disclosure burden. It communicates that the tool changes which signed-in account is active for ordering and that the account must already exist. However, it does not disclose what happens when the label is invalid, whether the switch is persistent, or what the return response is, so transparency is only partial.
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 short sentences with no filler. The core action and prerequisite are front-loaded, and every clause contributes to correct tool usage.
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?
For a simple state-selection tool with two well-documented parameters and no output schema, the description covers the essential behavior and prerequisite. It does not explain error behavior or how the switch affects subsequent order calls, but the context provided is adequate for correct invocation in most cases.
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 description coverage is 100%, so the schema already documents both parameters including the 'work' label example. The description adds the work/personal context and the restaurant-scoped ordering intent, but it does not meaningfully extend the parameter semantics 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 uses a specific verb ('Choose') and a clear resource (which signed-in account to order with), with a concrete example (work vs personal). It also distinguishes itself from login/logout by explicitly requiring the account to already be signed in.
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 clearly states the prerequisite condition: the account must already be signed in via login. This implicitly tells the agent not to use this tool before login and suggests login as the alternative when the account is not yet signed in. It does not explicitly mention using list_accounts to discover valid labels, but the condition is still actionable.
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 distinct action and resource: order status vs. order staging vs. order placement, vendor discovery vs. menu retrieval, and login initiation vs. code submission. There is no overlap or ambiguity; even sequential tools like prepare_order and place_order are clearly separated by their purpose.
All tool names follow a consistent verb_noun pattern with lowercase and underscores (e.g., check_order, list_vendors, prepare_order). Even the login and logout tools fit the pattern as single verbs, and there is no mixing of conventions.
With 10 tools, the surface is well-scoped for a restaurant ordering domain. It covers discovery, menu browsing, account management, login flow, order staging, placement, and status checking without feeling bloated or too thin.
The core ordering lifecycle is covered end-to-end (vendor -> menu -> login -> prepare -> place -> check). The only noticeable gaps are the absence of an explicit address-saving tool (though referenced in the login description) and no order cancellation or update capability, though these may be out of scope for typical ordering workflows.
