evaluate_signup_bonusIs a credit card sign-up bonus worth it? Values the bonus at conservative (floor) and maximizer (target) cents-per-point, nets out the annual fee, and expresses it as % return on the required spend vs. a flat 2% cash-back card.
| Name | Required | Description | Default |
|---|---|---|---|
| program | Yes | ||
| annualFee | No | ||
| bonusPoints | Yes | ||
| requiredSpend | Yes |
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?
Annotations already declare readOnlyHint=true, so the description doesn't need to repeat that. It adds useful behavioral detail by describing the calculation logic: valuing at floor vs target rates, netting the annual fee, and comparing to a 2% cash-back card. It doesn't cover edge cases (e.g., missing annualFee), but the read-only safety profile is covered by annotations.
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 a single efficient sentence that front-loads the purpose with a clear question. It packs the essential methodology into ~30 words with no filler, earning its place.
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 4 parameters, no output schema, and zero param descriptions in the schema. The description conveys the core calculation and output concept (as % return), but it leaves input semantics vague (especially 'program') and doesn't specify the exact return format or behavior when optional inputs are omitted. Adequate for a simple evaluator but with clear gaps.
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 description coverage is 0%, so the description must compensate for undefined parameters. It explicitly mentions 'annual fee' and 'required spend', and 'bonus' implies bonusPoints, but the required 'program' parameter is not described at all. The description does not explain how program affects point valuation or what the optional annualFee behavior is.
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 evaluates credit card sign-up bonuses, using a specific methodology (floor and target cents-per-point, annual fee netting, % return vs 2% cash-back). It distinguishes from siblings like calculate_points_or_cash and get_points_values by its focus on sign-up bonus valuation.
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 opening question 'Is a credit card sign-up bonus worth it?' provides clear context for when to use this tool. It implies this is for evaluating sign-up bonus offers rather than general point conversions or valuations, but it does not explicitly exclude alternatives or mention when not to use it.
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.
Each tool serves a distinct purpose: deal browsing (list_live_deals, get_deal), valuation (get_points_values), decision calculators (calculate_points_or_cash, evaluate_signup_bonus), workflow guidance (get_points_hacking_playbook), membership info, transfer partner lookup, and subscription. There's no overlap—get_deal and list_live_deals are complementary (list vs. detail).
All tool names follow the same verb_noun pattern with snake_case (e.g., calculate_points_or_cash, get_membership_info, lookup_transfer_partners). The verbs vary but are all action-oriented, and the nouns clearly indicate the resource, resulting in a predictable and consistent naming scheme.
With 9 tools, the server is well-scoped for its purpose—covering deal discovery, detailed evaluations, reference data, workflow guidance, and user subscription. Each tool earns its place without redundancy, and the count is within the ideal range for clarity.
The toolset covers the full lifecycle of a points-hacking user: discover deals, get valuations, decide cash vs. points, evaluate sign-up bonuses, transfer partners, follow the playbook, and subscribe for alerts. No critical operations are missing, and the playbook tool orchestrates the workflow to handle gaps like flight search externally.