get_points_valuesSunny's Points valuation reference: conservative (floor) and maximizer (target) cents-per-point for every credit card, airline, and hotel program. Pass program for one row, omit for the full table.
| Name | Required | Description | Default |
|---|---|---|---|
| program | 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?
Beyond the readOnlyHint annotation, the description reveals how the tool behaves with different inputs: providing a program returns one row, while omitting it returns the full table. This adds useful behavioral context that isn't in the schema or annotations, though it doesn't specify behavior for invalid programs.
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 two sentences: the first defines what the tool is, the second explains usage mechanics. Every sentence earns its place with no redundancy or fluff.
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 read-only reference tool, the description fully covers the data content (cent-per-point values with floor/target), output variations (row vs full table), and usage. Annotations confirm read-only, and no output schema is needed for this level of clarity.
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 parameter 'program' has no schema description, but the description explains its meaning and effect: it selects a single row for a specific program, and defines the program domain as credit card, airline, or hotel. This compensates for the 0% schema coverage.
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's function: a valuation reference providing conservative (floor) and maximizer (target) cents-per-point for all programs. It distinguishes itself from siblings by being a reference table rather than a calculation or deals tool, with an explicit scope ('credit card, airline, and hotel program').
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 implies usage (e.g., 'Pass program for one row, omit for the full table') but does not explicitly state when to choose this tool over alternatives like calculate_points_or_cash or lookup_transfer_partners. It offers usage mechanics but lacks direct comparative guidance.
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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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.