Search publicly shared name-picker wheels already created on spinwheelnames.com by title (e.g. "classroom", "baby names", "raffle"). Returns links a user can open to view or reuse an existing wheel.
| Name | Required | Description | Default |
|---|---|---|---|
| limit | No | Max results to return (1-20, default 5). | |
| query | Yes | Search text to match against wheel titles. |
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, the description carries the full burden of behavioral disclosure. It does state that the search covers publicly shared wheels and that the tool returns openable links, which is useful, but it omits details like result ordering, match behavior, or what happens when no wheels are found. This is adequate but not thorough.
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 with no filler. The core action and resource are front-loaded, examples are compact, and the return type is stated clearly. Every sentence earns its place.
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 search tool with two parameters and no output schema, the description is nearly complete: it explains the input intent and the output type. It could add a note about empty-result behavior or relevance ordering, but the existing text is sufficient for correct invocation in most cases.
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 coverage is 100%, so the baseline is 3: both query and limit are already documented in the schema. The description adds a small amount of context by noting the search targets wheel titles and providing example queries, but it does not meaningfully extend the schema's parameter explanations.
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 the specific verb 'Search' with a clear resource: publicly shared name-picker wheels on spinwheelnames.com. It specifies the match target (title) and gives concrete examples, making it distinct from siblings like get_wheel, spin_wheel, and pick_random_number.
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 clearly implies this tool is for finding existing public wheels by title, but it does not explicitly state when not to use it or name alternatives such as get_wheel for retrieving a specific wheel by ID. The usage context is clear but exclusion criteria are left to inference.
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.
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.