subscribe_deal_alertsSubscribe the user to Sunny's Points free deal-alert emails. ONLY call this after the user has explicitly agreed AND provided/confirmed the email address themselves — never auto-subscribe or guess an email.
| Name | Required | Description | Default |
|---|---|---|---|
| Yes | |||
| homeAirport | No |
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 provide readOnlyHint=false, idempotentHint=true, destructiveHint=false, and openWorldHint=true. The description adds useful context by explaining that this subscribes the user to free emails and emphasizes the consent requirement, which is beyond what annotations convey. It does not contradict any annotations and adds meaningful external-side-effect context.
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 sentence that front-loads the core action and then adds a critical usage condition. It is highly concise with no wasted words, achieving good structure and readability.
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?
For a simple tool with two parameters and no output schema, the description covers purpose and a key prerequisite, but it omits any explanation of the homeAirport parameter and does not describe what happens after subscription (e.g., confirmation behavior). While annotations cover safety aspects, the missing parameter context makes the description only minimally complete.
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 provides only the email parameter format and homeAirport as a string, with 0% description coverage. The description clarifies that the email must be user-provided/confirmed, which adds some semantic constraint, but it does not mention homeAirport at all. This leaves parameter meaning incomplete, especially for the optional homeAirport field.
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 action: 'Subscribe the user to Sunny's Points free deal-alert emails.' It uses a specific verb ('subscribe') and resource ('deal-alert emails'), and it is distinct from all sibling tools, which focus on points calculations, deals, and membership info.
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 gives explicit when-to-use instructions: 'ONLY call this after the user has explicitly agreed AND provided/confirmed the email address themselves.' It also provides a strong when-not-to-use: 'never auto-subscribe or guess an email.' This is clear guidance for invocation.
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.