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Get a quote

concierge_walk

Walk a shop's concierge one turn at a time and get a real quote. Omit walk to start (you get the first question and a walk id); pass walk + answer for each turn. The shop mails the quote at the end, so answer the email question with an address the person you are shopping for actually reads. Deterministic: no model on our side, and the whole conversation is sealed as a receipt the shop owner sees.

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
shopYesthe shop's slug
walkNothe walk id from a previous call — omit to start a new conversation
answerNoyour answer to the question the last call asked

Schema Changelog

Changes observed during successful MCP inspections.

  1. First observed

TDQS

A4.4/5.0
Behavior5/5

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

Beyond the annotations, the description discloses meaningful behavioral traits: the tool is deterministic ('no model on our side'), the conversation is sealed as a receipt visible to the shop owner, and the quote arrives by email rather than as a direct return value. The email warning about using an address the recipient actually reads is especially valuable. No contradiction with the annotations.

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?

Three dense sentences with no filler: purpose first, then the exact call pattern, then the critical email/receipt caveats. Every sentence earns its place and the structure is easy to scan.

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

Completeness5/5

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

Despite having no output schema, the description explains the response format for both the initial call and follow-up turns, how to know when the quote arrives, and important side effects. For a stateful, multi-turn tool this is a complete and actionable definition.

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

Parameters4/5

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

Schema coverage is 100%, so the baseline is 3, but the description adds important stateful semantics: `walk` is omitted to start and required to continue, `answer` pairs with `walk`, and the flow 'you get the first question and a walk id' clarifies what each call produces. This goes beyond simple parameter descriptions.

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 verb and resource — 'Walk a shop's concierge one turn at a time and get a real quote' — and clearly separates initiating versus continuing the conversation. It does not explicitly name sibling alternatives like concierge_ask or market_walk, so it stops short of full differentiation.

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?

It gives concrete usage direction: omit `walk` to start, then pass `walk` + `answer` for each subsequent turn. The intended scenario (multi-turn quote gathering via a shop's concierge) is clear, though it never explicitly states when not to use this tool or names alternative tools.

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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