get_scorecardGet the full scorecard for a golf course: all tee sets with hole-by-hole par, yardage, stroke index, and front/back nine ratings. Available for thousands of courses with scorecard data.
| Name | Required | Description | Default |
|---|---|---|---|
| slug | Yes | Course full_slug, e.g. "new-jersey/monmouth-county/red-bank/navesink-country-club" |
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
With annotations already declaring read-only, non-destructive behavior, the description adds valuable context by detailing the output content and the limitation that scorecard data may not exist for all courses.
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, front-loaded with the core purpose and followed by a brief note on availability. Every word contributes meaningful information with no redundancy.
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?
Since there is no output schema, the description usefully enumerates the returned data elements, giving the agent a solid understanding of the tool's output. It could mention edge cases or error responses, but the level of detail is adequate for a simple read-only tool.
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 fully describes the sole parameter with an example, so the description adds no additional parameter semantics. Per the rubric, baseline 3 applies when schema coverage is high.
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 gets a full scorecard for a golf course, enumerating the specific content (all tee sets, hole-by-hole par, yardage, stroke index, ratings). This distinguishes it from sibling tools like get_course and get_reviews.
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 implicit usage context by focusing on scorecard data and noting availability for thousands of courses, but it does not explicitly discuss when to choose this tool over alternatives or 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.
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.