Spin Wheel Names

Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?

There are no annotations, so the description carries the full burden. It does disclose that this is a read/fetch operation and that entries are included in the result. However, it does not describe the return shape, failure behavior, or any other side effects, which leaves a moderate gap.

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 a single well-structured sentence: it names the action, the target resource, the identifier type, and the included content with no filler. Every part 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 one-parameter read operation, the description is nearly complete: it says how to identify the wheel and what is returned. It does not specify the exact response format, but given the lack of an output schema and the low complexity, this is 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?

Schema description coverage is 100%, and the id parameter is already described as the wheel slug or custom id from a spinwheelnames.com URL. The description mostly restates that selector and adds 'including its entries', adding little semantic value beyond the schema. Baseline 3 applies.

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 (Fetch) and clearly identifies the resource: a specific saved wheel from spinwheelnames.com, addressed by slug or id, with entries included. This distinguishes it from siblings like search_wheels and spin_wheel right away.

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 the intended use: when you already have a wheel slug or custom id from a spinwheelnames.com URL and want the full saved wheel. It does not explicitly list when not to use it or name alternative tools, but the context is clear enough.

Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.

Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?

With no annotations, the description carries the full burden, and it does disclose the core behavior: a random whole number with inclusive bounds and default values. It does not mention side effects, error handling, or randomness guarantees, but for a simple stateless number picker this is adequate though not rich.

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 concise sentences with the core operation front-loaded and no filler. The wheel metaphor and default summary both earn their place without bloating the text.

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 low-complexity tool with two optional parameters fully documented by the schema, the description provides enough context: what the tool returns, the inclusive range behavior, and defaults. It could mention edge cases like min > max, but nothing essential is missing for normal invocation.

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 schema already documents min and max with defaults and inclusivity. The description adds only a summarized 'Defaults to 1-100' and the inclusive-range concept, which is already present in the schema, so no significant extra semantic value is added.

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 action ('pick'), the resource (a random whole number), and the key constraints (inclusive range, default 1-100). It is easily distinguished from the sibling wheel tools because it returns a number rather than operating on a wheel.

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 wheel analogy implies when a user would want this kind of random pick, and the defaults give immediate invocation context. However, it does not explicitly say when to use this tool over get_wheel/search_wheels/spin_wheel or provide exclusion criteria, so routing guidance is only implied.

Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.

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.

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.