See which brands offer the most rewards, have the most active programs, or the best cashback rates. Useful for finding where to shop for maximum value.
| Name | Required | Description | Default |
|---|---|---|---|
| limit | No | Number of brands to rank. Defaults to 10, max 10. | |
| category | No | Filter by category (e.g. "retail", "dining", "travel", "gaming"). |
Changes observed during successful MCP inspections. Dates show when Glama detected each change.
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
No annotations are provided, so the description carries full burden. It mentions ranking by rewards, active programs, or cashback rates, but does not explain how these criteria are selected or combined, given that the schema has no metric parameter. It also does not disclose whether the operation is read-only, what the output looks like, or any other behavioral details.
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 brief (two sentences) and front-loaded with the action. It is free of fluff, though the second sentence restates the value proposition of the first. Overall, it is well-structured and easy to scan.
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?
The tool has no output schema and no annotations, so the description should explain return values and behavior. It does not describe the output format, and the ambiguity about how the ranking criteria work leaves a significant gap. The description is adequate for a basic sense of purpose but incomplete for an agent to confidently invoke the tool without expecting surprises.
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?
Schema coverage is 100%, with both parameters (limit and category) having descriptions. The tool description adds no parameter-specific detail beyond this. Since the schema already provides full coverage, the baseline of 3 is appropriate.
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 the tool's function: viewing brands with the most rewards, active programs, or best cashback rates. The verb 'see' is a bit weak, but the resource (brands) and ranking context are clear. It distinguishes from some siblings like 'best_deals' but does not explicitly differentiate from 'leaderboard' or 'compare_brands'.
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 explicitly provides a use case: finding where to shop for maximum value. This gives clear context for when to use the tool. However, it does not mention any exclusions or alternatives, such as using 'compare_brands' for head-to-head comparisons or 'best_deals' for finding deals.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
