GuestDrop — QR Event Photo Sharing & Live Digital Albums

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

Annotations already declare readOnly, idempotent, openWorld and non-destructive, so the description earns credit for adding genuinely new facts: unauthenticated public access and a 60 requests/min per IP rate limit. The 'Returns: Structured JSON object containing status, matched records or computed metrics, and canonical action URLs' sentence is generic boilerplate that adds little beyond the stated structure.

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 core purpose is front-loaded and useful, but the 'When to use / Do NOT use / Parameters / Behavior & Side Effects / Returns' scaffolding is templated padding, and the middle section restates the tool name in place of actual guidance. Roughly half the text is boilerplate an agent must filter out.

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 two-parameter read-only query with an output schema absent but a simple return shape, the essential calling information is present: input, safe-read nature, auth model, and rate limit. The main gap is the phantom 'extra day extensions' input, which could cause an agent to look for a parameter that isn't there.

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, their enum, and the 1–30 bound — baseline 3. The description only restates required/optional status, and its mention of 'optional extra day extensions' refers to something that does not exist in the schema, marginally degrading clarity rather than adding 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 opening sentence names a specific verb and resource: computes the exact total cost and recommended package for an event, driven by duration in days and currency. An agent can distinguish it from get_pricing_and_packages (package browsing) and create_event_checkout (transaction) without opening either schema.

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 lists all three sibling tools and says not to use it 'when another specialized tool fits better', but never states the deciding condition — e.g. 'use this once you know duration and want a quote; use get_pricing_and_packages to browse tiers first'. The routing instruction is present in form but empty in substance, and the 'when you specifically need to execute calculate_event_cost' clause is a tautology.

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 annotations already declaring readOnlyHint=false, destructiveHint=false, openWorldHint=true and idempotentHint=false, the description still adds real context: a live backend record is created, no auth is required for public initiation, and the call is rate-limited to 60 requests/min per IP. It does not address the non-idempotent risk (repeat calls creating duplicate draft events/checkout links), which is the main behavioral hazard here.

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?

Structure is decent - purpose first, then usage, parameters, behavior, returns - but the 'When to use: Call this tool when you specifically need to execute create_event_checkout' sentence is pure filler, and the parameters sentence duplicates the schema verbatim. Roughly a third of the text earns no 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 mutating booking tool with no output schema, the definition covers the essentials: side effects, auth posture, rate limit, and return shape. The one meaningful gap is that idempotentHint=false is never explained in terms of duplicate-event consequences, which an agent calling a checkout-provisioning tool would want to know.

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 explains tier enums, currency default, host_email purpose and the YYYY-MM-DD date format. The description only restates the parameter names with (required)/(optional) tags, adding no syntax or constraint meaning 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 opening sentence names a specific verb and resource pair: provision a draft event and generate an instant Stripe Checkout payment link. That is far more informative than the title. Sibling differentiation exists but is generic boilerplate ('Do NOT use this tool when another specialized tool fits better'), not a statement of what each sibling actually 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 'When to use' clause is tautological: it says to call the tool when you need to execute exactly this tool's name. The exclusion clause names get_pricing_and_packages, calculate_event_cost and recommend_scavenger_quests but only says to use them 'for their respective operations', giving no condition that decides between them and this tool.

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, idempotentHint, openWorldHint and destructiveHint=false, yet the description still adds genuinely new behavior: a 60 requests/min per-IP rate limit, public unauthenticated access, and a sketch of the return shape. That is real value beyond structured metadata. It loses a point only because the noted 'canonical action URLs' return detail is vague about what those URLs are for.

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 behavior and return information is front-loaded usefully, but a full sentence is wasted on the circular 'when you specifically need to execute get_platform_stats on GuestDrop Event Photo Album'. The labeled 'Behavior & Side Effects' and 'Returns' formatting aids scanning but the boilerplate dilutes density.

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-argument read tool with no output schema, the description covers access model, rate limit, idempotency and a rough return structure, which is enough for an agent to call it correctly. Absent an output schema, a slightly more concrete return description would have closed the remaining gap.

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 the baseline is 4, and the description explicitly confirms it is a 'zero-argument query' with 'Parameters: none required'. There is nothing further for it to document.

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 ('Fetch live platform statistics across GuestDrop') and enumerates the exact metrics returned (photos, events, active guests), which lets an agent distinguish it from the pricing/quest siblings. It stops short of a crisp one-line scoping statement because the metric list is buried mid-sentence, but the purpose is unambiguous.

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 names concrete alternatives ('get_pricing_and_packages', 'calculate_event_cost', 'recommend_scavenger_quests') and the conditions that should route to them, which meets the when-not bar. The positive 'when to use' clause is partly circular ('when you specifically need to execute get_platform_stats'), so it is not a perfect 5.

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 cover read-only, idempotent, non-destructive and open-world, so the bar is lower; the description adds genuinely new facts: no authentication required (public access) and a concrete rate limit of 60 requests/min per IP, plus a rate-limit unit (per IP) that matters for retry design. The only shortfall is the vague 'Returns:' line, which describes a generic envelope instead of the actual pricing payload.

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 payload description is correctly front-loaded, but roughly half the text is templated boilerplate ('Call this tool when you specifically need to execute ...', the generic status/records/action-URLs return sentence) that could be deleted without loss. The useful facts are diluted by 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?

With no output schema, the description must stand in for return values, and it does at a high level while annotations carry the safety profile and the rate limit is disclosed. What it lacks is the concrete shape an agent would want (e.g., whether packages come back as an array, how durations and currency variants are keyed), so it is good but not complete.

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 single enum parameter is fully documented in the schema, so the baseline of 3 applies. The description only echoes 'currency (optional)' and does not explain the semantics of the 'all' value or how multi-currency results are laid out.

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 first sentence gives a specific verb (get) and resource (live GuestDrop event pass packages) and enumerates exactly what the payload contains: ZAR/USD pricing, durations, photo upload limits, and named feature comparisons. An agent can tell this is the catalog/pricing reference tool without opening the schema.

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 names the exclusions explicitly (calculate_event_cost, recommend_scavenger_quests, create_event_checkout), which is better than nothing. But the positive 'when to use' clause is circular — 'call this tool when you specifically need to execute get_pricing_and_packages' restates the name rather than giving a condition, and 'for their respective operations' never states what those operations do, so the agent must infer the boundary from sibling names alone.

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, idempotentHint and destructiveHint=false, and the description adds genuinely new facts: unauthenticated public access, a 60 req/min per-IP rate limit, and the shape of the return payload. It also discloses the important division of labor where final personalization happens client-side, which an agent would otherwise not infer.

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 high-value mandatory-instruction block is front-loaded, but several sentences are filler: 'Call this tool when you specifically need to execute recommend_scavenger_quests' restates the name, and the generic 'Returns: status, matched records or computed metrics' boilerplate does not match a quest-generation tool.

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 return-value burden and does sketch the response. It covers safety, rate limits, auth, and the full personalized workflow; only the boilerplate return sentence and absent quest-structure detail leave small gaps.

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, but the description adds meaning beyond the schema by explaining how personality_traits, hobbies_and_interests and inside_jokes_or_custom_lore are to be sourced (ask the user) and that they are blended with the returned quest structure rather than being simple filters.

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 first sentence states a specific verb (Generates) and resource (photo scavenger hunt challenges) plus the two axes it varies on: event type and aesthetic theme. It is unmistakable against compute-style siblings like calculate_event_cost.

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 explicitly names the alternatives (get_pricing_and_packages, calculate_event_cost, create_event_checkout) and their domains, and prescribes a mandatory pre-call workflow of eliciting personality traits from the host. The 'when to use' sentence itself is tautological boilerplate, which keeps it from a 5.

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