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Alpha4-Labs

Loyalteez MCP Server

by Alpha4-Labs

loyalteez_update_leaderboard_stats

Update user stats after rewarding them to keep leaderboard rankings current. Call after every reward distribution so LTZ earnings reflect promptly.

Instructions

Update user statistics after rewarding them. This keeps leaderboard data current. Call after every reward distribution.

See also: loyalteez://docs/shared-services/leaderboard-service

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
brandIdNoYour brand wallet address. If not provided, uses LOYALTEEZ_BRAND_ID environment variable.
platformYesPlatform: "discord" | "telegram" | "web" | etc.
claimTypeNoType of claim/event (e.g., "daily_checkin", "purchase")
ltzAmountYesLTZ amount earned
displayNameNoUser's display name for leaderboard
userIdentifierYesUser identifier (platform_userId@loyalteez.app or email)

Schema Changelog

Changes observed during successful MCP inspections.

  1. First observedv1.1.0

TDQS

A3.7/5.0
Behavior3/5

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

With no annotations, the description carries the full disclosure burden. It adds ordering semantics (must run after reward distribution) and the effect on leaderboard currency, but is silent on idempotency, whether stats are additive, required permissions, and failure behavior for a mutation tool.

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 sentences plus a doc link, all front-loaded with the action first and the timing constraint second. No filler or repetition.

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?

The doc resource reference partially compensates, but for a write tool with no annotations and no output schema, an agent still lacks idempotency, permission, and failure-mode guidance needed to call it safely in automated reward flows.

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 all six parameters are already documented in the schema, and the description adds no parameter-level meaning beyond what is there. Baseline 3 applies when the schema does the heavy lifting.

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?

States a specific verb and resource ('Update user statistics') and ties it to a concrete outcome ('keeps leaderboard data current'). An agent can distinguish it from the read-side sibling get_leaderboard, though it doesn't explicitly differentiate itself from other update/write siblings like update_achievement_progress or track_event.

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?

'Call after every reward distribution' gives an explicit trigger condition tied to a sibling activity (rewarding). It clearly says when to invoke, but offers no alternatives or exclusions for the case where another update tool might be the right choice.

Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.