get_courseGet full details for a specific golf course by its URL slug. Returns name, location, type, holes, architect, year built, amenities, contact info, ratings, slope stats, and nearby courses.
| 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?
Annotations already cover the read-only, idempotent, and non-destructive nature. The description adds valuable context by enumerating the returned fields (name, location, type, holes, architect, etc.), giving the agent a clear expectation of the response content without needing an output schema.
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?
A single, front-loaded sentence states the purpose and then lists return fields efficiently. Every word contributes to understanding, with no redundancy or filler.
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?
Despite lacking an output schema, the description fully discloses the expected return payload by listing the key data categories. The tool is simple (one parameter, no nested objects), and the description covers all essential aspects for correct invocation and response interpretation.
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 input schema already provides full coverage of the single parameter 'slug' with a detailed example. The description only repeats 'by its URL slug' and adds no additional semantic meaning, so a baseline score of 3 is appropriate given the schema's completeness.
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 a specific verb ('Get'), resource ('full details for a specific golf course'), and identifier ('by its URL slug'). It distinguishes itself from sibling tools like search_courses and find_nearby_courses by focusing on a direct lookup by unique slug.
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 phrase 'by its URL slug' clearly indicates that this tool is used when you already have the slug and need comprehensive details. It does not explicitly mention alternatives or when-not-to-use, but the context is clear for a typical get-by-id pattern.
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.