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rsnc_agent_browse_perks

Search for deals, discounts, and rewards across all brands or filter by category and budget. Find cashback offers, free products, exclusive access, and experiences from retail, dining, travel, and more.

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
limitNoMaximum number of perks to return. Defaults to 20.
brandIdNoScope to a specific brand. If omitted, returns deals from all active brands. Use rsnc_agent_list_brands to find brands.
categoryNoFilter perks by category.
maxPriceNoMaximum reward cost to include in results.

Schema Changelog

Changes observed during successful MCP inspections. Dates show when Glama detected each change.

  1. First observed

TDQS

A3.7/5.0
Behavior3/5

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

With no annotations provided, the description carries the full burden of explaining behavior. It conveys a read-only search action without mentioning side effects, authorization, or return format. The lack of explicit non-destructive confirmation or pagination details is a slight gap, but the language 'search' and 'find' strongly implies a safe, read-only operation.

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?

The description is a single, front-loaded sentence that efficiently communicates both the main action and key filters. No redundant words or filler; it earns its place entirely.

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 tool has four optional parameters, no output schema, and no annotations. The description gives a good high-level overview but does not mention what the result set looks like, whether pagination is supported (though 'limit' hints at it), or how results are ordered. For a read-only search tool, this is adequate but leaves some room for improvement.

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?

The schema already describes all four parameters with 100% coverage, so the description adds little new meaning. It does reinforce 'category' and 'budget' (mapping to category and maxPrice), but does not elaborate on limit or brandId beyond what the schema provides. Baseline 3 is appropriate.

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?

The description clearly states the tool's function: searching for deals, discounts, and rewards across all brands with optional filters by category and budget. It distinguishes itself from sibling tools like rsnc_agent_brand_perks (brand-specific) and rsnc_agent_best_deals (curated picks) by emphasizing broad cross-brand discovery.

Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.

Usage Guidelines3/5

Does the description explain when to use this tool, when not to, or what alternatives exist?

The description implies usage for browsing or discovering perks, especially when the user wants a broad search rather than brand-specific or targeted deals. However, it does not explicitly state when to use this tool over alternatives or provide exclusions, leaving the guidance somewhat implicit.

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

B3.3/5.0
Disambiguation2/5

Several tools have overlapping purposes, e.g., rsnc_agent_best_deals, rsnc_agent_route_purchase, and rsnc_agent_compare_cashback all help find the best purchase/reward option. Similarly, rsnc_agent_network_info, rsnc_agent_network_stats, and rsnc_agent_network_analytics provide similar network overview data with unclear boundaries.

Naming Consistency3/5

All tools share the consistent 'rsnc_agent_' prefix, but the remainder mixes verb-first patterns (browse_perks, claim_reward, create_event) with noun-first patterns (brand_analytics, network_flows, perk_intelligence). This inconsistency makes the tool surface less predictable than a uniform verb_noun scheme.

Tool Count2/5

With 45 tools, the server feels over-scoped for a rewards network. While the domain is broad, many tools are highly granular analytics variations, and the count exceeds the 25+ threshold, adding cognitive load and diminishing coherence.

Completeness4/5

The tool set covers the core lifecycle: brand discovery, onboarding, event/perk creation and updates, reward processing, user balance/stats, and redemption. Minor gaps exist, such as no delete operations for events/perks and no direct user listing, but agents can work around these.

Resources