Activate or deactivate a reward in the catalog. Use to manage reward availability based on analytics.
| Name | Required | Description | Default |
|---|---|---|---|
| is_active | Yes | true to activate, false to deactivate | |
| reward_id | Yes | UUID of the reward |
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
No annotations are provided, and the description does not disclose behavioral traits beyond the basic action. It is a mutation tool (activate/deactivate) with no mention of side effects, reversibility, permissions, or consequences for active rewards. This is a significant gap given the absence of annotation support.
Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.
Is the description appropriately sized, front-loaded, and free of redundancy?
The description is two short sentences that are front-loaded with the verb and object. Every word earns its place; there is no fluff or redundancy.
Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.
Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?
For a simple two-parameter mutation tool with full schema coverage, the description provides sufficient context: what it does, when to use it, and the parameters are self-documented. It lacks any note on return values or error conditions, but given the simplicity and lack of output schema, the description is reasonably complete.
Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.
Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?
The schema describes both parameters with 100% coverage, including 'true to activate, false to deactivate' for is_active. The description adds little beyond the schema, as 'activate or deactivate' mirrors the schema's boolean meaning. Baseline 3 is appropriate since the schema carries the semantic load.
Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.
Does the description clearly state what the tool does and how it differs from similar tools?
The description clearly states 'Activate or deactivate a reward in the catalog' with a specific verb and resource, and it distinguishes from sibling tools like create_reward, list_rewards, and redeem_reward by focusing on availability toggling. The phrase 'based on analytics' further clarifies the intended use case.
Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.
Does the description explain when to use this tool, when not to, or what alternatives exist?
The description provides clear context on when to use the tool ('Use to manage reward availability based on analytics') but does not explicitly mention when not to use it or name alternative tools. This falls short of being explicit about exclusions but is still clear in its intended application.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
Add one secure layer between your agents and this server.
Several tools have overlapping purposes, such as mint_loyalty_tokens vs earn_points (both mint loyalty tokens with fee bundles), create_loyalty_program vs register_loyalty_program (deploy vs register existing token), and activate_loyalty_program vs update_program_status (both manage program status). Some pairs like check_voucher_status and list_gift_certificates also overlap on voucher/certificate tracking. However, descriptions are detailed enough to reduce ambiguity for careful agents.
Most tools follow a consistent verb_noun snake_case pattern (e.g., create_reward, list_loyalty_programs). Subtle deviations include earn_points vs mint_loyalty_tokens (different verbs for similar mint operations) and use_voucher vs redeem_reward (different verb styles for redemption). Overall, the naming is predictable and understandable.
With 39 tools, this server is bloated for a loyalty platform. The addition of Bazaar discovery/payment tools and report management expands the scope, but many tools overlap or cover minor variations (e.g., two workflow planners: generate_program_defaults and get_program_workflow_status). A leaner set of 20-25 tools would be more appropriate.
The tool surface covers the full loyalty program lifecycle: creation, activation, registration, token minting/transfer, rewards, gift certificates, vouchers, offers, customer export, analytics, and reports. Notable gaps include no CRUD for personalized offers (only create), no edit capability for rewards (only status changes), and no direct function to list all vouchers by merchant (only status check by code). These are minor workarounds.