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

Annotations already declare readOnlyHint=true, so safety is covered. The description adds useful behavioral context: the temporal range and the home-park default. However, it doesn't disclose return format, pagination, or edge cases such as invalid pak names or timezone handling. Given the annotation coverage, a score of 3 is appropriate.

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 three short sentences, each earning its place: it states the core function, the default behavior, and the intended use case. It is front-loaded and free of 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?

For a read-only tool with two optional parameters and no output schema, the description fully covers what an agent needs: what the tool returns, the temporal scope, and the default park. The schema and annotations cover the remaining details.

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 has 100% description coverage for both parameters, including defaults, so the description doesn't need to add parameter-level detail. The description does restate the park default, but adds no new meaning for parameter handling beyond what the schema already says. 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 ('Get') and resource ('operating hours for a Six Flags park') with a clear scope ('from today forward'). It distinguishes itself from sibling tools by focusing on operating hours, not wait times, attractions, or shows. The home park default is also explicitly stated.

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 gives a concrete use case ('plan what time to arrive and how long you have'), which clearly communicates when to invoke this tool. It doesn't explicitly describe when not to use it or name alternatives, but the presented context is clear and sufficient.

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?

Annotations already declare readOnlyHint=true, so the description adds value by disclosing the default park (Carowinds) and the operating-day population caveat. These details inform the agent about expected data availability and default behavior without contradicting annotations.

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 compact and front-loaded with the core purpose, followed by two useful behavioral details. Every sentence earns its place; there is 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?

For a simple read-only tool with one optional parameter and no output schema, the description plus annotations cover all necessary context: what it does, the default park, and when data is available. Nothing critical is missing for an agent to invoke it correctly.

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 covers the single park parameter 100%, with identical default information. The description adds no new parameter-specific meaning beyond what the schema already provides, so the baseline score of 3 is appropriate.

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 states a specific action ('Get today’s live show schedule') and identifies the resource ('showtimes for a Six Flags park'), including the types of shows (parades, stunt shows, character meets). This clearly differentiates from siblings like wait times or generic park schedule.

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 clear context: it is for today's showtimes and defaults to the home park. It also notes that showtimes are only populated on operating days. It doesn't explicitly name alternatives or when-not-to-use, but the specific show focus makes the intended use clear.

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?

The readOnlyHint=true annotation covers the safety profile, and the description adds useful behavioral details: it returns all attractions, includes statuses, sorts longest-wait first, and includes a crowd-level summary. It does not disclose every edge case, but it goes beyond the annotation meaningfully.

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 dense sentence that packs in the operation, default behavior, return contents, ordering, and crowd summary without filler. Every clause adds information an agent would need.

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?

With no output schema, the description takes the responsibility of explaining the return shape: attraction statuses, standby and single-rider waits, sorting, and crowd level. The optional parameters are directly explained by the input schema. Nothing essential for selecting and invoking the tool is missing.

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 documents both parameters at 100% coverage, including the default for park and the openOnly behavior. The description mostly reiterates what the schema says without adding deeper param-level meaning. This meets the baseline for high schema coverage but does not elevate it.

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 ('Get') with a clear resource ('current ride wait times for a Six Flags park'), and immediately distinguishes itself from sibling tools like sixflags_get_park_schedule and sixflags_list_attractions. It also states scope and return structure, leaving no ambiguity about what the tool does.

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 makes the use case clear: retrieve current ride wait times. It also notes the default park behavior, which helps agents know when no park parameter is needed. It does not explicitly contrast with alternatives, but the purpose is distinct enough among the siblings that an agent can select it correctly without explicit exclusions.

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?

Annotations declare readOnlyHint=true, and the description confirms a read-only health check. It adds specific return information (status and park count). No behavioral contradictions or missing critical details beyond what annotations cover.

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, front-loaded sentence with no extraneous words. It efficiently conveys the tool's purpose and output.

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?

Given the tool's simplicity (no parameters, no output schema), the description fully covers its behavior. It explains the inputs (none) and outputs (status and park count), making it complete for agent understanding.

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 has no parameters, so the description does not need to elaborate. With zero parameters, the baseline is 4, and the description provides no additional parameter information, which is acceptable.

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 identifies the tool as a health check for the upstream data source, specifying the action (check reachability and data return) and what it returns (ok/degraded, number of parks). It is distinct from sibling tools that retrieve specific park data.

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 implies the tool is used to verify connectivity before other operations, but it does not explicitly state when to use it versus alternatives or provide exclusions. Usage is implied rather than explicitly guided.

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?

Annotations already declare readOnlyHint=true, so the safety profile is covered. The description adds useful behavioral context beyond that: the data is static metadata rather than live status, and the result includes map coordinates. This gives the agent a clearer expectation of what it will receive.

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 three focused sentences with zero filler. The core action and scope are front-loaded, and the most important caveat (static vs. live) is placed immediately after the main behavior.

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 read-only listing tool with two optional parameters and no output schema, the description covers the essential facts: what is listed, what the output includes, how it differs from live data, and the default park. Nothing critical is missing for an agent to invoke it correctly.

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%, so the schema already documents both parameters. The description adds a small amount of context by stating the park defaults to the home park (Carowinds) and mirrors the schema's default of ATTRACTION for type. It does not need to repeat parameter details, so the baseline 3 is appropriate.

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 states a specific verb and resource: 'List the attractions (or shows / restaurants) at a Six Flags park' and clarifies it returns 'the full directory' with map coordinates. It also differentiates itself from live-status tools by calling itself 'Static metadata', which helps an agent distinguish it from sixflags_get_wait_times.

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 tells the agent when NOT to use this tool for live data, steering it to 'sixflags_get_wait_times for current waits.' It also notes the default park behavior. It does not mention the similarly-named sibling sixflags_get_shows, but the directory scope is clear enough for most selection cases.

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?

Annotations already declare this tool read-only, and the description adds useful behavioral context: it covers the combined chain, includes water parks, returns owning destination, and flags the configured home park. There is no contradiction, and no dangerous hidden side effects are suggested.

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?

Single well-structured sentence that front-loads the main purpose, includes clarifying examples, describes the optional filter, and lists return fields. Every clause adds useful information without 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?

For a simple read-only listing tool with one optional parameter and no output schema, the description is complete: it names the resource, scope, filtering behavior, and return contents. An agent can decide to call it and understand the result without additional documentation.

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 search parameter is already well documented. The description restates that filtering is optional and by name substring, but does not extend meaning much beyond the schema, only adding the broader context of park/destination naming.

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?

States a specific action and resource: 'List Six Flags parks', naming the combined chain and examples. It clearly identifies the tool's domain, distinguishes it from list_attractions and other sibling tools, and details the returned fields.

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 makes the tool's role obvious and explains optional name-substring filtering, but it does not explicitly state when to choose this tool over siblings such as sixflags_list_attractions or sixflags_get_park_schedule. Usage is implied rather than directly contrasted with alternatives.

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?

The readOnlyHint annotation already signals no mutation, and the description adds meaningful behavioral context: it operates on 'currently-operating rides,' uses only 'standby wait' rather than other queue types, and is intended for repeated use during a day. No contradiction with annotations.

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 compact, front-loads the core purpose in the first clause, and every sentence earns its place: purpose, ranking behavior, optional filters, usage guidance, and default park. There is 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?

For a read-only suggestion tool with fully documented parameters, the description covers what the tool does, how to use it, and the park default. There is no output schema, and the description does not explicitly describe the response shape, but the ranking-by-wait phrasing and limit parameter make the expected output reasonably inferable.

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%, so the schema already documents all four parameters. The description adds contextual meaning around the main use case—'exclude rides you've already done' and 'cap the wait'—but does not substantially extend the param-level documentation, keeping this at the baseline.

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 opens with a specific verb and resource: 'Recommend which attraction to ride next,' then defines the behavior precisely as 'ranks currently-operating rides by shortest standby wait.' This clearly distinguishes it from sibling tools like get_wait_times or list_attractions, which return raw data rather than a recommendation.

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?

It gives explicit guidance on when to use it: 'Use this repeatedly through the day to keep hopping to the lowest-wait ride.' It also notes optional exclusions and caps and states the park default. It does not explicitly name alternatives or say when not to use it, but the use case is clearly scoped.

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