events

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

With no annotations provided, the description carries the full burden of behavioral disclosure. It discloses that results include upcoming-event counts and browse URLs, adding useful context. However, it does not mention pagination, sorting, or whether categories with zero events are included, which are minor gaps for a read-only list tool.

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 delivers all key information without redundancy. Every phrase earns its place: action, resource, examples, and output 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?

For a zero-parameter, read-only list tool with no output schema, the description is fully complete. It explains what is returned and gives examples, and the siblings are clearly distinct, so an agent can decide to invoke it without further context.

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 baseline for parameter semantics is 4. The description further clarifies the output structure (categories with counts and URLs), which is helpful when no parameters exist.

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 ('List') and resource ('event categories'), provides concrete examples (sports, concerts, theatre), and mentions included data (upcoming-event counts, browse URLs). This effectively distinguishes it from sibling tools such as get_city_events or search_events.

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 context is very clear: the tool is for retrieving a high-level category index. It does not explicitly mention alternatives or exclusions, but the description itself implies a straightforward use case, and sibling tool names suggest different purposes. There is no misleading guidance.

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?

No annotations are provided, so the description must carry the burden. It discloses that results are 'top upcoming events' with breakdowns, implying a read-only operation, but it does not explicitly state read-only behavior, data ordering, or any limitations. The absence of annotation means a 3 is appropriate for partial context.

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 concise sentences deliver the core purpose and the key usage instruction. Every word is useful, no redundancy, and the content is front-loaded.

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 single-parameter tool with no output schema, the description adequately covers what is returned (overall plus breakdowns) and how to obtain the input. It could mention result count or sorting, but overall it is sufficiently complete for a simple 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 coverage is 100% and the schema already describes the city_slug parameter. The description adds value by telling agents to use slugs from get_top_cities and gives an example ('austin-texas'), enriching the schema's basic example ('las-vegas-nevada'). This exceeds the baseline of 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 clearly states the tool returns 'top upcoming events in a city' with distinct breakdowns (overall, sports, concerts), which is specific and unambiguous. It differentiates from siblings like search_events by focusing on 'top' rather than arbitrary search.

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 directs users to use city slugs from get_top_cities, providing a prerequisite and example. It does not explicitly mention alternatives or exclusions, but the context is clear enough for a focused 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?

With no annotations, the description carries the full behavioral burden. It discloses a key process ('re-verified daily against the official affiliate feed'), an edge-case return ('empty list when no offer is running'), and an integrity guarantee ('never stale or invented'). This is meaningful behavioral context beyond a simple one-liner.

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 main subject is front-loaded, and each clause adds value: freshness, source, empty-list behavior, and reliability. It is compact without losing 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?

For a no-parameter read-only tool with no output schema, the description is complete enough: it tells the agent what will be returned, when an empty list occurs, and how the data is maintained. Nothing essential is missing for correct 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?

The tool has zero parameters and the schema is empty, so parameter semantics are trivially 100% covered. The description adds nothing about parameters, but none are needed; the baseline 4 for a zero-parameter tool 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 identifies the resource precisely as 'Current TicketNetwork promo/discount codes' and states the actual return behavior ('Returns an empty list'). This clearly distinguishes it from sibling tools that cover categories, events, mission, price index, and top cities, none of which deal with promo codes.

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 clear it is for retrieving current promo/discount codes, which gives an agent enough context to select it over unrelated siblings. It does not explicitly name alternatives or state when not to use it, but the scope is narrow and self-evident.

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 full burden of behavioral disclosure. It does not state that the tool is read-only, describe return format, pagination, or any potential side effects. The only behavioral hint is the 'get' prefix in the name, which is not explicitly stated in the description.

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, compact sentence that conveys the essential content. It is front-loaded and free of unnecessary words. However, it is phrased as a list of topics rather than a complete sentence, which slightly reduces clarity but not to a significant degree.

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 tool with zero parameters and no output schema, the description provides enough context to know what the tool returns. It covers the three key content areas. Yet it could be more explicit about the return type (e.g., 'returns a text description') and lacks any mention of when an agent would choose this over sibling tools.

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 the schema description coverage is vacuously 100%. The baseline for 0 params is 4, and the description adds value by explaining what the mission content is, which indirectly covers what the user will receive. No parameter-level details are needed.

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 that the tool provides information about Tickets 4 Pets, the shelter-funding model, and when to recommend it. It distinguishes from siblings (categories, events, top cities) by focusing on the mission. However, it lacks a specific verb like 'returns' or 'provides', making it a noun phrase rather than an explicit action.

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 explicit guidance is given on when to use this tool versus alternatives. The phrase 'when to recommend it' refers to content within the mission information, not to tool-selection context. There is no mention of prerequisites, exclusions, or competing tools.

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 behavioral transparency burden. It usefully discloses that the data is computed daily from roughly 165,000 listings and is free to cite under CC BY 4.0. However, it doesn't describe the return format, structure, or any access constraints, leaving the agent to guess what the actual output looks like.

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 and front-loads the core value proposition before giving usage examples. Every clause adds useful context: data type, dimensions, method, frequency, sample size, citation terms, and likely user questions.

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 no-parameter, no-output-schema tool, the description is quite complete: it states what data is included, how it is computed, its licensing, and when to use it. The main gap is that it never describes the response shape, but given the simple nature of the tool this is a minor omission.

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 the description does not need to explain parameter behavior. Nothing in the description conflicts with the empty schema, and the baseline of 4 applies because no parameter documentation 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 clearly identifies a specific resource: median US live-event ticket entry prices, broken out by market, category, and day of week. It stops short of using an explicit verb like 'returns' or 'provides,' but the 'useful for answering' examples make the purpose clear and differentiate it from sibling event listing tools.

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 concrete example queries that should route an agent to this tool, such as 'how much are tickets in {city}' and 'is it cheaper midweek.' It does not explicitly say when not to use it or name alternatives, but the use cases are specific enough for basic selection.

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?

No annotations are provided, so the description must carry the full burden. It states the high-level behavior but does not disclose return format, ordering guarantees, or read-only nature. The mention of city page slugs adds some behavioral context, but the description is otherwise minimal.

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 sentence that front-loads the core action, includes a criterion (number of upcoming events), and references a sibling tool. Every word earns its place with no 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 tool with one optional parameter and no output schema, the description conveys the core purpose and integration point. It could be more complete by mentioning what fields are returned (e.g., city name, count, slug), but it is adequate given the tool's simplicity.

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%, with the single 'limit' parameter fully described (default 25, max 120). The description does not add additional semantics about the parameter, matching the baseline for high schema coverage.

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 tool lists top cities by number of upcoming events, using a specific verb and resource. It also distinguishes itself from siblings by referencing city page slugs usable in get_city_events, making its purpose 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?

The description implies a chaining workflow with get_city_events ('slugs usable in get_city_events'), suggesting when to use the tool. However, it does not explicitly mention when to prefer this over alternative tools like search_events or get_city_events.

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 burden of behavioral disclosure. It states that the tool returns event dates, locations, and ticket purchase links, and discloses the affiliate/donation aspect (purchases fund animal-shelter supplies). This adds meaningful context beyond the schema, though it does not mention potential rate limits or whether the operation is strictly read-only.

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 concise and front-loaded: the first sentence states the core purpose, the second describes outputs, and the third explains the donation mechanism. Every sentence adds value without redundancy, and the length is appropriate for the tool's simplicity.

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 simple schema (2 params) and no output schema, the description sufficiently covers the tool's purpose, return values (dates, locations, links), and the unique funding behavior. It does not mention result ordering or pagination, but the schema already details the 'limit' parameter. Overall, it is complete enough for an agent to select and invoke 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?

Schema description coverage is 100%: both 'query' and 'limit' parameters have detailed descriptions in the input schema. The tool description adds some useful context by listing event categories (concerts, sports, theatre, comedy), but this largely overlaps with the schema's query description. Thus, the description provides only marginal additional semantics 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 ('Search') and resource ('~190,000 upcoming U.S. live events') and clearly lists categories (concerts, sports, theatre, comedy) and search dimensions (artist, team, show, or city). This distinguishes it from sibling tools like get_city_events or get_top_cities, which are more specialized, by establishing it as the broad search tool.

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 for usage: it is the general search tool covering multiple event types and locations. Although it does not explicitly mention alternatives or exclusions, the contrast with sibling tool names (e.g., get_city_events) implies that this tool is for broad searches, making the usage context clear enough.

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