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davidmosiah

Rappi MCP (unofficial)

Preview Rappi checkout

rappi_checkout_preview
Read-onlyIdempotent

Preview your Rappi order totals before checkout without being charged. Get a read-only summary of costs to verify the final amount before placing an order.

Instructions

Totals preview. Does not charge. Read-only.

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
address_idNo
privacy_modeNo
response_formatNomarkdown
payment_method_idNo
Behavior3/5

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

Annotations already declare readOnlyHint, idempotentHint, and destructiveHint=false, so the agent knows it's safe. The description adds 'Does not charge' and 'Read-only', which reinforces the annotations but adds little beyond them. It doesn't describe whether the preview reflects the current cart state, or whether it requires an active cart. Given the strong annotations, the description adds marginal value, so a 3 is appropriate.

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

Conciseness4/5

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

The description is extremely short, which is good for token efficiency, but it is under-specification rather than conciseness. Each word earns its place ('Totals preview. Does not charge. Read-only.'), and the key safety property is front-loaded. However, a few more words on parameters would not hurt.

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

Completeness2/5

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

For a tool with 4 parameters, 0% schema coverage, and no output schema, the description is incomplete. The agent does not know what the output will look like, whether the preview is for the current cart or a specific checkout, or how the privacy_mode and response_format affect results. The annotations cover safety, but not operational details. This is a moderately complex tool that needs more context.

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

Parameters2/5

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

Schema description coverage is 0%, so the description carries the full burden for explaining parameters. It fails entirely. The tool takes address_id, payment_method_id, privacy_mode, and response_format, but the description mentions none of them. The agent must guess what address_id and payment_method_id mean, and which arguments are optional. There is no default behavior explained. This is a significant gap.

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 states a specific action ('Preview') and resource ('Rappi checkout'), which is clear enough to distinguish it from order-related tools like get_order or track_order. It could be more explicit that it previews the checkout totals for the current cart, but the title and description align well. It doesn't name a sibling, but the purpose is unambiguous.

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 gives no guidance on when to use this tool versus alternatives. It doesn't mention that it should be called before placing an order, or that it's useful for confirming prices. There is no mention of when not to use it (e.g., not for actual purchase). Sibling tools like place_order or get_cart are not referenced. The agent is left to infer the use case.

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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