get_leaderboardGet the top golf courses in a state by category: hardest (highest slope), easiest (lowest slope, public only), or longest (most yardage). Great for answering "hardest course in NJ" type questions.
| Name | Required | Description | Default |
|---|---|---|---|
| limit | No | Number of results | |
| state | Yes | 2-letter state code, e.g. "NJ" | |
| category | No | Ranking category | hardest |
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
Annotations already declare read-only/idempotent behavior, so the description adds value by defining category semantics (e.g., 'easiest' is public-only) and the metrics used (slope, yardage). This goes beyond the schema's enum labels.
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, front-loaded with the main purpose, and every clause adds value. No filler or redundant wording.
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 read-only leaderboard tool with three simple parameters, the description covers purpose, usage scenarios, and category definitions. It doesn't specify the return format, but the low complexity and lack of output schema make this acceptable.
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 covers 100% of parameters, but the description enriches the meaning of the 'category' parameter by explaining what each value represents. It also gives a state code example, adding practical context.
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 retrieves top golf courses ranked by category (hardest, easiest, longest), with specific criteria for each. This distinguishes it from siblings like search_courses or get_course by emphasizing leaderboard-style ranking.
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 provides a concrete use case ('hardest course in NJ' questions) and explains the category criteria, making it clear when to use this tool. It doesn't explicitly name alternatives, but the context is well-defined.
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.
find_nearby_courses and search_courses overlap significantly, both returning courses near a location with mileage, and search_stymie adds another ambiguous entry point. However, each tool has a distinct specialization (exact distance vs. general search, cross-collection discovery).
All tool names follow a consistent verb_noun snake_case pattern (build_, find_, get_, search_), making the API predictable and easy to navigate.
13 tools is a reasonable size for a golf data server covering courses, equipment, architects, trips, and reviews. The slight overlap between search tools could be consolidated, but the count is not excessive.
The domain is well-covered for read-only data (courses, scorecards, reviews, equipment, architects), but tee times are a placeholder and trip building relies on external verification. This leaves notable gaps in functionality that agents must work around.