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rsnc_agent_user_recommendations

Get personalized perk recommendations for a user, scored by persona affinity and affordability. Requires HMAC authentication.

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
limitNoMaximum number of recommendations to return. Defaults to 5.
userIdYesThe user identifier (email or wallet address).

Schema Changelog

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

  1. Added

TDQS

A4/5.0
Behavior4/5

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

With no annotations provided, the description carries the full burden of behavioral disclosure. It discloses an important security requirement ('Requires HMAC authentication') and explains the recommendation scoring logic ('by persona affinity and affordability'). This goes beyond the basic schema, though it does not detail pagination, error behavior, or return format. For a read-only operation, this is reasonably transparent.

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 two short sentences. The first sentence front-loads the primary purpose with a clear verb and object, and the second sentence adds a critical auth requirement. Every word earns its place, and there is no fluff or redundancy.

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?

The tool is simple with only two parameters and no output schema. The description covers purpose, scoring methodology, and authentication. It does not explain the output shape, but for a 'recommendations' endpoint that returns a list, this is likely self-evident. Given the low complexity, the description is nearly complete, though it would benefit from a note about what the returned recommendations look like or any default behavior for invalid userIds.

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 input schema already provides 100% coverage with descriptions for both 'limit' and 'userId', so the schema does the heavy lifting. The description does not add any additional meaning about the parameters themselves. Per the calibration rules, the baseline is 3 due to high schema coverage, and the description does not compensate with extra parameter context.

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: 'Get personalized perk recommendations for a user, scored by persona affinity and affordability.' This uses a specific verb ('Get'), a resource ('perk recommendations for a user'), and distinguishes it from sibling tools by mentioning personalized scoring criteria. It is not a tautology and clearly indicates the tool's scoped purpose.

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 does not explicitly mention when to use this tool versus alternatives, nor does it provide exclusion criteria. The phrase 'for a user' implies the target use case, but there is no guidance on when not to use it or how it compares to sibling tools like rsnc_agent_best_deals or rsnc_agent_suggest_perks. The context is clear but lacks explicit alternatives.

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.

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