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Faceabot — tell it your need, get the tool that works

Where to buy: stores verified for shopping agents

find_shop
Read-only

Finds online stores that Faceabot checked for AI shopping agents: not blocked, agent checkout available (UCP), and whether the price agents read matched the store (price truth record). Use when: you shop for a user and need a store for a product or brand. Do not use when: you need a software tool (use solve_need). Example: {"query":"running shoes","agent_checkout_only":true}

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
limitNoHow many stores (1-50)
queryNoProduct, brand or store words, e.g. perfume, running shoes
agent_checkout_onlyNotrue = only stores declaring agent checkout (UCP)

Schema Changelog

Changes observed during successful MCP inspections.

  1. Added

TDQS

A4.2/5.0
Behavior4/5

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

Annotations already establish readOnlyHint=true, openWorldHint=true and destructiveHint=false, so safety is covered. The description goes beyond them by explaining what 'checked' means in practice (not blocked, agent checkout declared, price matched), which tells the agent what quality guarantee it is getting. It does not cover result volume or paging behavior.

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 content is front-loaded (what it finds, then when to use, when not to use, then a compact example) with no filler sentences. Every clause either defines scope or aids selection.

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?

For a zero-required-parameter, read-only discovery tool with no output schema, the description conveys enough: what is searched, what verification attributes come back, and how to narrow with agent_checkout_only. It stops short of describing the returned store record fields, which is the only remaining gap.

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 the schema already documents all three optional parameters, making 3 the baseline. The description adds only a concrete invocation example ('running shoes' + agent_checkout_only) rather than new meaning for limit or query syntax. Adequate but not additive over the schema.

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 ('Finds online stores that Faceabot checked') and qualifies it with three concrete verification criteria (not blocked, agent checkout via UCP, price truth record). It also routes away from a sibling by naming solve_need. The only weakness is jargon ('UCP', 'price truth record') that an agent must infer, but the core 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 Guidelines5/5

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

It gives an explicit positive trigger ('Use when: you shop for a user and need a store for a product or brand') and an explicit exclusion with a named alternative ('Do not use when: you need a software tool (use solve_need)'). This is exactly the when/when-not/alternative structure that lets an agent route without opening sibling schemas.

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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