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ANIME INTELLIGENCE / AI PURCHASE INTELLIGENCE

Record Purchase Intent

record_purchase_intent

Record that an AI agent selected a product or merchant as a purchase candidate. Use after shopping_intelligence when the agent has chosen an offer and is preparing to hand off or execute the purchase.

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
amountNo
currencyNo
decisionNo
merchantNo
agent_typeNo
merchant_idNo
canonical_product_idYes

Output Schema

TableJSON Schema
NameRequiredDescriptionDefault
statusNo
next_actionNo
purchase_intent_idNo

Schema Changelog

Changes observed during successful MCP inspections.

  1. Added

TDQS

A3.8/5.0
Behavior3/5

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

The annotations already declare a non-read-only, non-idempotent, open-world write, so the agent knows the safety profile and that repeat calls may duplicate. The description confirms the write intent ('record') and adds workflow sequencing, but says nothing about persistence, whether records can be updated/removed, or permission requirements. Modest added value over the structured hints.

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?

Two tight sentences, zero filler, with the action front-loaded and the sequencing constraint immediately after. Every clause carries information.

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

Completeness3/5

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

An output schema exists, so return values need not be described, and annotations cover the safety profile. However, for a 7-parameter write tool with 0% schema coverage, the description leaves the meaning and expected format of most inputs unclear, which is a real gap for correct invocation.

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% across 7 parameters, so the description carries the full burden and largely fails. It only loosely gestures at the 'product or merchant' concepts (mapping vaguely to canonical_product_id and merchant) while leaving amount, currency, decision, agent_type, and merchant_id entirely unexplained.

Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.

Purpose5/5

Does the description clearly state what the tool does and how it differs from similar tools?

States a specific verb+resource ('Record ... purchase intent') and clarifies the semantic scope as 'an AI agent selected a product or merchant as a purchase candidate', which differentiates it from generic logging tools. It explicitly positions itself relative to the sibling shopping_intelligence, so an agent can distinguish the two without opening schemas.

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?

Gives an explicit trigger ('after shopping_intelligence when the agent has chosen an offer and is preparing to hand off or execute the purchase'), naming the preceding sibling tool. It lacks any when-not guidance or mention of alternative recording tools, so it falls just short of the top score.

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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