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

What changed in AI agent commerce

whats_new
Read-only

Faceabot's watch robots track standards (MCP, A2A, UCP, ACP), platforms (OpenAI, Google, Anthropic, Shopify), payments, and which major stores let shopping agents in. Returns the latest items with links. Use when: you need to know what changed recently before acting (new protocol version, a store that now blocks agents, a platform rule). Example: {"topic":"standards","days":7}

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
daysNoHow many days back (1-365, default 14)
topicNoOptional filter: standards, platforms, commerce, payments or access (stores letting agents in)

Schema Changelog

Changes observed during successful MCP inspections.

  1. Added

TDQS

A3.9/5.0
Behavior3/5

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

Annotations already declare readOnlyHint=true, openWorldHint=true and destructiveHint=false, so the safety profile is covered by structured data. The description's main behavioral addition is the return shape ('latest items with links'), which is useful given there is no output schema, but it says nothing about freshness/cadence, caching, or rate limits for a live-watching feed.

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 short lines with a clean progression: what it tracks and returns, when to use it, and a call example. Every sentence earns its place and the differentiating substance (source coverage) is front-loaded.

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 two-optional-parameter, read-only tool with no output schema, the description covers purpose, trigger conditions, and return shape adequately. The remaining gap is cadence/freshness of the data, which matters for a 'what changed recently' feed but is not addressed.

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 description coverage is 100%, so the two optional parameters are already documented, and the enum values for topic are enumerated in the schema. The inline example ({"topic":"standards","days":7}) adds genuine value by showing the parameters used together and clarifying that both are filters over the same feed, going slightly beyond the schema baseline.

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 concrete resource (recent changes across standards, platforms, payments, and agent-access store policies) and a concrete output ('latest items with links'), which is far more specific than the tool name 'whats_new'. It implicitly separates itself from action-oriented siblings like find_shop or check_reliability by framing itself as a changelog/feed rather than a query or action tool, though it never names an alternative.

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 an explicit 'Use when:' clause with three concrete triggering situations (new protocol version, a store that now blocks agents, a platform rule), which is clear decision context. It stops short of stating when NOT to use it or naming sibling tools it overlaps with (e.g. faceabot_status), so it earns a 4 rather than a 5.

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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