get_program_analytics
Get analytics for your loyalty programs
Input Schema
TableJSON Schema
| Name | Required | Description | Default |
|---|---|---|---|
No arguments | |||
Loyal Spark
Schema Changelog
Changes observed during successful MCP inspections. Dates show when Glama detected each change.
- First observed
TDQS
B3.1/5.0
Behavior2/5
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
With no annotations, the description carries the full burden. It does not disclose whether the tool is read-only, requires authentication, returns a report, or has any side effects. Minimal behavioral transparency.
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?
A single, front-loaded sentence with no redundant filler. It states the action and the resource efficiently.
Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.
Completeness2/5
Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?
Despite the simple schema, the absence of an output schema and any details about what analytics are returned makes the description inadequate. It would benefit from mentioning typical metrics, time ranges, or the return format.
Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.
Parameters4/5
Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?
The tool has zero parameters, so there is nothing to explain. The description adds no param semantics, but the baseline for 0-param tools is 4, and the schema already covers this trivially.
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?
The description uses a clear verb ('Get') and resource ('analytics for your loyalty programs'), which distinguishes it from siblings like get_platform_stats or get_program_workflow_status. However, it doesn't specify what kind of analytics, so it's not maximally precise.
Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.
Usage Guidelines2/5
Does the description explain when to use this tool, when not to, or what alternatives exist?
No guidance is provided about when to use this tool versus alternatives. The description only states what it does, not when to prefer it over other analytics or program tools.
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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TDQS
B3.4/5.0
Disambiguation3/5
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.
Naming Consistency4/5
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.
Tool Count2/5
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.
Completeness4/5
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.