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Alpha4-Labs

Loyalteez MCP Server

by Alpha4-Labs

loyalteez_check_eligibility

Validate whether a user can receive a reward for a specific event before tracking it. Returns eligibility status, cooldown info, and claim history.

Instructions

Check if a user is eligible to receive a reward for a specific event. Returns eligibility status, cooldown info, and claim history. Use this BEFORE tracking an event to validate the user can receive the reward.

Implementation Note: Eligibility checking requires event configuration (maxClaims, cooldown, reward) and user claim history. If the endpoint is unavailable, eligibility can be determined by:

  1. Calling loyalteez_get_event_config to get event settings

  2. Checking user's claim count against maxClaims

  3. Verifying cooldown period has elapsed

See also: loyalteez://docs/api/rest-api

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
brandIdNoYour brand wallet address. If not provided, uses LOYALTEEZ_BRAND_ID environment variable.
eventTypeYesEvent type to check eligibility for
userEmailYesUser's email address

Schema Changelog

Changes observed during successful MCP inspections.

  1. First observedv1.1.0

TDQS

A4.1/5.0
Behavior4/5

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

With no annotations, the description carries the full burden and does reasonably well: it enumerates what comes back (eligibility status, cooldown info, claim history) and discloses a degraded-mode workaround via get_event_config. It stops short of stating read-only/non-mutating semantics explicitly or any auth requirements.

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?

Front-loads the core purpose in the first sentence, then adds return contents and a fallback procedure. The implementation note is long but earns its place by enabling a manual fallback; still, it could be tightened.

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?

There is no output schema, so the description usefully names the return fields, and it points to the REST API doc reference for deeper detail. The main missing piece is how it relates to the sibling perk-eligibility tool.

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?

All three parameters are already documented at 100% schema coverage, including the brandId env-var fallback and eventType/userEmail meaning. The description adds no parameter-level detail beyond the schema, so the baseline 3 applies.

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 (check) and resource (eligibility for a reward on a specific event), and explicitly distinguishes it from the tracking path by saying to use it BEFORE loyalteez_track_event. An agent can tell it apart from the other event-mutating siblings.

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 clear usage timing: call before tracking an event to validate reward eligibility, plus a fallback procedure when the endpoint is unavailable. It does not, however, distinguish this from the near-neighbor loyalteez_check_perk_eligibility, so the alternative selection is incomplete.

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