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AgentPay

by io.github.zapnikita95
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get_user_preferences

Read-only

Get the user's category preferences (fat %, brands, sizes, clothingGender, pets, sport macros, etc.), schema, learned signals, and onboardingPurposes. Call before search_products only when the owner already asked to buy or to look in AgentPay. Do not fetch prefs for idle advice («какие витамины попить»). For apparel/clothing: read clothingGender (male|female|unisex|any) — male = men's line only (no auto-unisex); female = women's + unisex; unisex only if set or owner said unisex explicitly. If clothingGender empty and owner did not say gender in the query, ask once then update_preference. For sportpit / protein / creatine / «запас на неделю» when buying: ALWAYS call with category=sport first; calculate BMR/TDEE/KBJU yourself; then search by proteinPer100g, servingSizeG, sportForm. Also call first when the owner says «Заполни предпочтения AgentPay» / «заполни предпочтения». Categories: dairy, grocery, apparel, pets, beauty, household, pharmacy, sport, gifts, kids, digital, electronics. Triggers: «мой бренд», «безлактозное», «заполни предпочтения». For «как обычно», «то же самое», «прошлый раз» call list_purchases first. If AGENTPAY_API_KEY required and you already have sessionId from this chat: pass sessionId and retry. Never begin_agent_link again. Never ask the owner to edit connector settings or reconnect. Never web-search.

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
categoryNoPreference category key
sessionIdNoOptional. Agent-link sessionId from begin_agent_link / poll_agent_link. Pass on every tool when connector has no Authorization Bearer (Grok/ChatGPT/Claude). After status=approved this authenticates as als_<sessionId>. Never invent a sessionId. Never ask the owner to open Authorization settings (Grok has none).

Schema Changelog

Changes observed during successful MCP inspections.

  1. Changed1 schema field changed
    • addedInput schema / properties / sessionId
      Added value: +{
+  "description": "Optional. Agent-link sessionId from begin_agent_link / poll_agent_link. Pass on every tool when connector has no Authorization Bearer (Grok/ChatGPT/Claude). After status=approved this authenticates as als_<sessionId>. Never invent a sessionId. Never ask the owner to open Authorization settings (Grok has none).",
+  "type": "string"
+}
  2. Changed1 schema field changed
    • removedInput schema / properties / sessionId
      Removed value: -{
-  "description": "Optional. Agent-link sessionId from begin_agent_link / poll_agent_link. Pass on every tool when connector has no Authorization Bearer (Grok/ChatGPT/Claude). After status=approved this authenticates as als_<sessionId>. Never invent a sessionId. Never ask the owner to open Authorization settings (Grok has none).",
-  "type": "string"
-}
  3. Changed1 schema field changed
    • changedInput schema / properties / sessionId / description
      Previous value: -"Optional. Agent-link sessionId from begin_agent_link / poll_agent_link. Pass on every tool when connector has no Authorization Bearer (Grok/ChatGPT/Claude). After status=approved this authenticates as als_<sessionId>. Never invent a sessionId. Never ask the owner to reconnect."New value: +"Optional. Agent-link sessionId from begin_agent_link / poll_agent_link. Pass on every tool when connector has no Authorization Bearer (Grok/ChatGPT/Claude). After status=approved this authenticates as als_<sessionId>. Never invent a sessionId. Never ask the owner to open Authorization settings (Grok has none)."
  4. Changed1 schema field changed
    • addedInput schema / properties / sessionId
      Added value: +{
+  "description": "Optional. Agent-link sessionId from begin_agent_link / poll_agent_link. Pass on every tool when connector has no Authorization Bearer (Grok/ChatGPT/Claude). After status=approved this authenticates as als_<sessionId>. Never invent a sessionId. Never ask the owner to reconnect.",
+  "type": "string"
+}
  5. First observed

TDQS

A4.9/5.0
Behavior5/5

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

Annotations already mark readOnlyHint=true and destructiveHint=false, but the description adds substantial behavioral context: authentication requirements (sessionId handling, never invent, retry logic), prohibitions (never begin_agent_link again, never ask to edit connector settings), and domain-specific logic (clothingGender interpretation, BMR/TDEE calculation). No contradiction with annotations.

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 long and dense, but every section adds necessary guidance for a complex tool with many edge cases. It is front-loaded with purpose and primary usage, then details. While it could be trimmed slightly, the structure is logical and justified by the tool's complexity.

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?

Given the tool's complexity, absence of an output schema, and minimal annotations, the description covers all necessary aspects: triggers, exclusions, authentication, category-specific logic, and actions like asking once then updating preferences. No critical information for correct invocation is missing.

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

Parameters5/5

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

The schema covers both parameters fully (100% coverage), but the description enriches semantics: it explains how 'category' values are used (dairy, apparel, sport, etc.) and when to pass 'sessionId' (when connector lacks Authorization Bearer), plus retry conditions. This goes well beyond the schema descriptions.

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?

The description clearly states the tool fetches user category preferences, schema, learned signals, and onboarding purposes, with a specific verb ('get') and resource. It distinguishes itself from siblings by specifying when to call it before search_products and contrasts with update_preference and list_purchases.

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?

Provides explicit when-to-use conditions (e.g., 'before search_products only when the owner already asked to buy or to look in AgentPay'), when-not-to (idle advice), and alternatives (list_purchases for 'как обычно', 'то же самое', 'прошлый раз'). Also includes detailed rules for sportpit and apparel scenarios.

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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