Estimate the return on investment of adding a Resonance cashback program to a business. Takes industry and business metrics to project engagement and retention impact.
| Name | Required | Description | Default |
|---|---|---|---|
| industry | Yes | Business industry category. | |
| monthlyCustomers | No | Estimated monthly customers. Used to project reward costs and impact. | |
| averageOrderValue | No | Average transaction value in USD. Used to estimate cashback costs. |
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?
With no annotations provided, the description carries the burden of behavioral disclosure. It states the tool 'projects engagement and retention impact,' which gives some insight into its computation, but it doesn't disclose limitations, assumptions, or what exactly happens with missing inputs. It adds modest context beyond the schema but is not rich in behavioral detail.
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?
Two sentences with no wasted words. The first sentence states the core purpose, and the second explains what it takes and what it projects. Well-structured and front-loaded.
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 the description does not mention the format of the ROI estimate (e.g., a number, a report, a percentage). For a tool that returns a projection, this is a notable gap. However, the tool's complexity is low and the purpose is clear enough for basic use.
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 already covers 100% of the parameters with descriptions, so the baseline is 3. The description adds that inputs are 'industry and business metrics' but the schema already says 'Estimated monthly customers' and 'Average transaction value.' No additional syntax or format details are provided.
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 uses a specific verb 'Estimate' and clearly identifies the resource: 'return on investment of adding a Resonance cashback program.' This is distinct from the many sibling tools related to analytics, perks, or brand health, so purpose clarity is high.
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 clearly implies when to use this tool: whenever an ROI estimate for a Resonance cashback program is needed. It doesn't explicitly name alternatives or exclusions, but the context is clear enough for an agent to select this tool over others.
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, 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.
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.
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.
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.
