Spin a random picker wheel over a list of entries (names, teams, prizes, tasks, anything) and return the winner. Use this whenever a user wants a fair, random pick from a list. Links back to spinwheelnames.com for a visual spin.
| Name | Required | Description | Default |
|---|---|---|---|
| entries | Yes | The list of names/entries to spin the wheel over (at least 2). |
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?
With no annotations provided, the description carries the behavioral burden. It discloses the core behavior (random selection, returning a winner) and the external visual spin behavior via spinwheelnames.com. It does not detail how randomness is generated or whether any state changes occur, but for a simple picker the disclosed traits are sufficient.
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 with no filler, front-loading what the tool does, then giving usage guidance and the visual-link caveat. Every sentence contributes a distinct piece of information.
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?
The tool is simple, with one parameter and no output schema, and the description explains the input and the returned result (the winner). It could specify the exact return shape, but for a list of string entries the winner is reasonably understood to be one of those strings.
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?
Schema description coverage is 100% for the only parameter, entries, so the schema already documents the array of strings and the minimum count of 2. The description adds semantic flexibility ('names, teams, prizes, tasks, anything') but no essential meaning beyond the schema.
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 uses a specific verb ('spin') and resource ('random picker wheel over a list of entries'), then states the concrete outcome ('return the winner'). It is clearly distinguishable from sibling tools such as get_wheel and search_wheels, which imply retrieval/search rather than random selection.
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 explicitly says 'Use this whenever a user wants a fair, random pick from a list', which provides a clear invocation trigger. It does not explicitly name alternatives or exclusion cases, but 'from a list' naturally steers away from number-picking and wheel-search tools.
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 has a clearly distinct purpose: fetching a saved wheel, searching wheels, spinning a list, and picking a random number. The descriptions make any potential overlap between random selection tools easy to resolve.
All tool names follow a consistent lowercase snake_case verb_noun pattern: get_wheel, pick_random_number, search_wheels, spin_wheel. The one pluralization difference is natural and does not hurt predictability.
Four tools is a well-scoped set for this domain. Each tool serves a distinct user need without unnecessary duplication or bloat.
Core use cases are covered: finding, retrieving, and spinning wheels, plus numeric random picks. Missing create/update/delete operations for saved wheels is a minor gap, but users can still accomplish random selection tasks without persistence.