mouseedge-skipper

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

The annotations only declare readOnlyHint=false, so the description must carry most of the behavioral burden. For a mutation that registers personal data, it does not disclose consent or privacy handling, idempotency, whether a pass can be claimed more than once, or what side effects occur after activation.

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 filler. The core action and benefits appear before the registration details.

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?

There is no output schema and the annotations are minimal, so the description should do more for a mutation that collects PII. It states what the pass unlocks but leaves out important context such as consent, data handling, repeat-claim behavior, and how this differs from redeeming a trip pass code.

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 six parameters thoroughly. The description mentions mobile number, email, and trip dates but omits name, partySize, and wantsTravelQuote, and it implies email and trip dates are part of registration even though only phone is required.

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 and resource: activate a 100% Free MouseEdge Starter Pass, and lists concrete benefits (3 concurrent watches and AI trip planning). It is clear what the tool does, but it does not distinguish it from nearby siblings such as redeem_trip_pass_code or compare_disney_apps_and_passes.

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 explains the mechanism (registering mobile, email, and trip dates) but gives no guidance on when to use this tool versus alternatives. It does not mention prerequisites, exclusions, eligibility, or how it differs from redeem_trip_pass_code.

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 establishes that this is a safe read operation. The description adds useful scope by naming the compared products and saying it returns pricing and upgrade links, but it does not disclose data freshness, link behavior, or other limitations.

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 efficient sentence with the comparison scope and return content front-loaded. Every clause contributes useful 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?

With no output schema, the description carries the burden of explaining return values, and it does state that pricing and upgrade links are returned. It could be more complete by describing the comparison output format or link handling, but it is adequate for a no-parameter read-only tool.

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 tool has zero parameters, so there are no parameter semantics to document. The baseline for a no-parameter tool is 4.

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, 'Compare', names the primary product and three competitors, and states the return content: 7-Day Trip Pass pricing and upgrade links. This clearly distinguishes it from sibling tools focused on alerts, waits, dining, and pass redemption.

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 for comparing apps and retrieving pass pricing, but it does not explicitly say when to use it instead of siblings like claim_free_starter_pass or redeem_trip_pass_code, nor does it state exclusions or prerequisites.

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 only declare readOnlyHint=false, confirming a mutation but nothing more. The description does not disclose that the alert persists and runs server-side, whether creating duplicates is allowed, any cost/pass requirements, or what happens on fulfillment — the most important behavioral facts for an 'armed' background watcher.

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 that packs the resource and its three use cases with no filler. Slightly promotional wording ('stakeout', 'in Disney Edge') is the only wasted space.

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 7 parameters fully documented in the schema and no output schema, most structural information is covered, but the description never says what is returned (watch ID? confirmation?) or how the created alert is subsequently managed, which matters for a stateful automation tool.

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 watchType enum already documents each mode and its mode-specific parameters (maxWait, autoBook, dineDate). The description adds no additional parameter meaning beyond the schema, so it sits 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?

States a concrete verb (Arm/create) plus the resource (an automated watch alert) and names the three flavors it covers: drop watch, Lightning Lane auto-booker, dining stakeout. It is distinguishable from read-oriented siblings like list_active_alerts or get_live_waits, though the marketing phrasing 'in Disney Edge' adds little.

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?

No guidance on when to create a watch versus polling get_live_waits or reading list_active_alerts, no mention of prerequisites (e.g. SKIPPER_PASS for autoBook), and no mention of limits or duplicate-watch behavior. The agent must infer all routing logic.

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 safety is covered. The description usefully discloses that the payload contains sensitive items — coach credentials and personalized ticket links — which hints at handling care, but it says nothing about whether the party data is cached, per-user, or how credentials are scoped. The title also advertises a 'Drop Wave Schedule' that the description omits, a minor inconsistency with the structured title.

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?

One compact sentence, front-loaded with the verb and the most important return item. Slightly compressed to the point of being a bare field list, with no framing sentence about what the profile represents.

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 carries the burden of explaining return shape, and it does so only as a short field list. It omits the drop/wave schedule promised by the title and gives no indication of the response structure (a single profile object vs. per-member entries) — enough to select the tool, not enough to predict its output.

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 tool takes zero parameters, so there is nothing for the description to disambiguate; baseline 4 applies. The description correctly implies a no-argument call returning the full profile.

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 ('Get') and enumerates the exact resources returned: traveling party roster, ticket links, preferred headliner rides, and MDE credentials. It is clearly distinguishable from siblings like get_live_waits or get_dining_availability. However, it never explains what the 'Benson party' is (a named group? the user's own party?), which leaves the scope slightly ambiguous.

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?

No guidance on when to call this versus alternatives, no prerequisites, and no exclusions. The description is a pure content list; an agent must infer that it should call this when it needs party/identity context rather than live operational data.

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 establishes that this is a safe read operation. The description adds useful context by specifying real-time walk-up waits, open party slots, and booking URLs, but it does not disclose pagination, rate limits, or other operational behavior.

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 wasted words. The examples are compact and directly support recognition of the target restaurants.

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 is largely complete: it states what data is returned and which domain it covers. It could be slightly stronger by explicitly noting the optional park filter or any freshness caveats, but the schema and annotations cover the remaining basics.

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 single optional parkSlug parameter is already fully documented in the schema. The description does not add any parameter-specific meaning beyond what the schema provides, which is acceptable at baseline 3.

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 names the exact resource: real-time walk-up wait times, open party slots, and booking URLs for Disney World table-service restaurants. The examples and 'dining' scope clearly distinguish it from ride-focused siblings like get_live_waits.

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 purpose implies when to use it, but there is no explicit guidance on when to choose this over alternatives or when not to use it. It also does not mention prerequisites or limitations, leaving usage to be inferred from the resource description.

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?

readOnlyHint=true already tells the agent this is a safe read, so the description's job is lower. It does add real value by disclosing return content (waits, downtime, tactical advice) that the empty schema and absent output schema cannot convey, but says nothing about refresh cadence, data staleness, or whether advice is model-generated.

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?

One front-loaded sentence with no filler; the core act and data types come first. The ten-attraction parenthetical is long but earns its place by defining scope that no structured field provides.

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 zero-param read tool with no output schema, the description covers what comes back (waits, downtime, advice) well enough to call it correctly. The main gap is that 'drop tactical advice' is undefined — the agent cannot tell whether that is a precomputed recommendation or a raw data field.

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?

Zero parameters, so the schema imposes no semantic burden and baseline 4 applies. There is nothing an agent could mis-supply.

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?

Specific verb (Get) plus enumerated resources (live wait times, downtime status, drop advice) scoped to a named list of ten tier-1 Disney headliners. The scope is unusually precise, but the description never names the obvious sibling get_live_waits, so the agent must infer the distinction itself.

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?

Usage is only implied: the enumerated attraction list signals 'use this for major rides,' and 'live' signals a real-time fetch. There is no explicit when-to-use / when-not clause and no mention of get_live_waits as the broader alternative.

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 establishes that this is a safe read operation. The description adds useful context by listing the data categories returned (wait times, status, trends, downtime flags) and the park scope. It does not disclose return format, pagination, freshness cadence, or rate-limit behavior, so it remains a moderate addition over 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 definition is a single front-loaded sentence that immediately states the action and resource. Every phrase carries useful scope or data-category information, and there is no redundant or filler 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 simple read-only tool with one optional parameter and no output schema, the description gives enough context to invoke it correctly: what it fetches, where it applies, and what categories of data it covers. It could be slightly more complete by hinting at the response structure or data freshness, but the essential invocation context is present.

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 single optional parkSlug parameter is already fully documented with examples. The description's list of park names provides scope context but does not add syntax, filtering behavior, or other semantics beyond what the schema already states.

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: real-time attraction wait times, status, trends, and downtime flags. It also names the covered parks, making the scope unambiguous. However, it does not explicitly distinguish this tool from siblings such as get_outages_and_reopens or get_headliners_alpha, so it falls short of a 5.

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 no explicit guidance on when to use this tool versus alternatives, nor does it state when not to use it. 'Real-time' implies a live-data context, but there is no mention of related tools, prerequisites, or 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 already declare readOnlyHint=true, so safety is covered. The description adds meaningful temporal scoping ('right now', 'earlier today'), which tells the agent the data is real-time and bounded to the current day. It says nothing about freshness/latency or how the two lists are structured in the response.

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 with no preamble, though 'broken down / down right now' is mildly redundant phrasing that could be tightened.

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 zero-parameter, read-only lookup with no output schema, the description covers what is queried and the time window. The main residual gap is not describing the shape of the returned outage/reopen entries, but it is otherwise sufficient to call the tool 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 tool takes zero parameters, so there is no parameter semantics to explain and the baseline of 4 applies; the description correctly neither invents nor omits argument guidance.

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?

Specific verb ('Check') plus a clearly scoped resource: attractions currently down and attractions that reopened earlier today. It is distinguishable from siblings like get_live_waits by focusing on outages rather than wait times, though it never names that distinction explicitly.

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 when the data is useful (checking current downtime or whether something came back up), but gives no explicit when-to-use/when-not guidance and does not point to any alternative tool such as get_live_waits for normal operating status.

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?

readOnlyHint=true already tells the agent this is a safe read with no side effects, so the description's job is lighter. It does add value by enumerating the returned data categories. However, with zero parameters the description never explains how the location is determined — presumably implicit from app/park context — which is a real behavioral unknown for a weather call.

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?

One sentence, front-loaded with the primary return values and ending with the highest-stakes item (severe weather/lightning advisories). Zero wasted words.

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 parameters and no output schema, the description needs only to state what comes back and it largely does. The remaining gap is the mismatch between the 'storm radar' name and the absence of any radar/imagery mention, plus the unstated implicit location.

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?

Zero parameters, so per the baseline this scores 4; there is nothing for the description to disambiguate. The only residual point is that the description does not confirm the implicit location assumption, but the empty schema already makes non-parameterization clear.

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 and enumerates the exact data returned: current temperature, feels-like, next-hour rain probability, and severe weather/lightning advisories. It is unambiguous against the theme-park siblings, none of which are weather tools. Minor gap: the tool name promises 'storm radar' but the description never mentions radar imagery, so the name slightly over-promises.

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?

There is no when-to-use guidance, no when-not-to-use guidance, and no mention of any alternative (e.g., create_watch_alert or list_active_alerts for alert subscriptions). The temporal scope ('next hour') is stated but never framed as a reason to pick this tool over something else.

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?

readOnlyHint=true already tells the agent this is a safe read. The description adds a meaningful scope qualifier — 'currently active and armed' — implying expired, paused, or fired alerts are excluded. Beyond that it discloses nothing about ordering, result limits, or whether phone scoping is required, so with annotations carrying the safety profile a 3 is warranted.

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?

One front-loaded sentence with no filler. The scope qualifier comes before the enumeration, so the most decision-relevant information lands first.

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, zero-required-parameter list tool with no output schema, the description covers what is returned (three alert categories) and the active-only filter. It falls just short of complete in not clarifying the phone-omitted behavior or default result scope.

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% and the single optional phone parameter is fully documented with an E.164 example. The description adds only the phrase 'for the user', which hints at a default to the authenticated account when phone is omitted, but it doesn't state that default explicitly. Baseline 3 applies when the schema does the heavy lifting.

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?

Specific verb plus concrete resource: 'List all currently active and armed' three named alert categories (drop watches, standby threshold alerts, dining reservation stakeouts). An agent can distinguish this from create_watch_alert purely on the list-vs-create axis, though the description never explicitly names a sibling to disambiguate further.

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 word 'active' and the enumeration of alert types imply the usage context (review what is currently armed), but there is no explicit statement of when to call this versus when to skip it, and no mention of the natural counterpart create_watch_alert or of what to do when the list is empty.

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 description provides strong behavioral context by explaining the resulting benefits of the redemption, such as enabling 'Twilio SMS alerts' and 'autonomous Lightning Lane auto-booking.' This adds significant value beyond merely stating that it is a non-read-only operation.

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?

It is a single, highly effective sentence that manages to introduce the action, the input types, the required platform context, and the primary value proposition without being verbose.

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 that no output schema is provided, the description is maximally complete by detailing the input types (code/phone) and the functional outcome of the redemption process.

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 description greatly improves parameter understanding by providing concrete, illustrative examples of valid codes (e.g., SKIPPER7, STAKEOUT7). It also implicitly defines the necessity of the 'phone' parameter for delivering the resulting SMS alerts and auto-booking notifications.

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's function (Redeem a 7-Day Trip Pass or Travel Agent promo code) and the immediate functional outcome (unlock Twilio SMS alerts and autonomous Lightning Lane auto-booking). It distinguishes itself by specifying the type of codes it handles.

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?

While it functions as a mutation tool, it doesn't explicitly state 'when' to use it relative to other similar redemption mechanisms. However, it provides helpful context by specifying it should be used 'directly inside ChatGPT or Claude.'

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