event-synoptic

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

No annotations are provided, so the description carries the full burden. It mentions included info (ratings, hours, happy hour details, links) but does not disclose any side effects, limitations, or error behavior. It gives adequate but not deep transparency.

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 long, front-loaded with the verb 'Find', and conveys all key capabilities without unnecessary 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?

Given 6 parameters, no output schema, and no annotations, the description covers the main use cases and return info. It lacks details on parameter constraints and error handling but is overall sufficient.

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 33% (only 2 out of 6 parameters have descriptions). The description adds context for vibe and happy_hour_now but does not fully compensate for the missing parameter documentation.

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 finds bars and nightclubs in Los Angeles with specific filters like vibe, location, and happy hour. It distinguishes itself from sibling tools that focus on events or tickets.

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 when to use the tool (finding bars with various criteria). It does not explicitly state when not to use it or offer alternatives, but the purpose is well-defined.

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

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

With no annotations, the description carries the full burden of behavioral disclosure. It explains inner workings such as the `free.today`, `free.label`, `free.next`, and `free.rules` fields, notes that free rules depend on the 'current LA date' (time-zone relevant), and lists additional return data (hours, open-now, visit length, quietest time, prices, links). This goes well beyond the schema and gives a detailed picture of 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 somewhat lengthy but is well-structured: it opens with the core purpose, then highlights two key differentiators with examples, and ends with a list of additional data points. Every sentence adds value, though it could be tightened without losing meaning. The front-loading is effective.

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 tool with no output schema, the description does a good job of enumerating return fields and filtering semantics. It covers free admission details, exhibitions, hours, open-now status, visit duration, quietest time, prices, and links. Missing details like pagination or error handling are non-critical for a finder tool, and the explanation of free rules is particularly thorough.

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 50% (type, limit, free_today have descriptions; lat, lon, open_now do not). The description adds meaningful context by enumerating museum types (matching the enum) and elaborating on the free-today concept. However, it does not explain lat/lon or open_now, nor does it fully compensate for the missing schema descriptions on those parameters. It provides partial value but leaves gaps.

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 begins with a specific verb and resource: 'Find Los Angeles museums and galleries'. It then lists concrete categories (art, history, science, film, kids, cars, gardens, photography) and highlights unique capabilities (free admission rules, exhibition schedules) that clearly distinguish it from sibling tools like find_bars or get_best_ticket_prices.

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 strong context for when to use this tool: 'Answers the two questions ticketing APIs cannot' (free admission and current exhibitions). This implies a contrast with ticketing-related tools such as get_best_ticket_prices, though it does not explicitly name alternatives or state when NOT to use it. The guidance is clear but not fully explicit.

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 carries the full burden. It discloses that Gametime prices are fetched LIVE and results are sorted, but fails to mention error handling, authentication needs, or the read-only nature. Adequate but not comprehensive.

Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.

Is the description appropriately sized, front-loaded, and free of redundancy?

The description is two sentences with no redundancy, front-loading the core purpose and adding key details about live pricing and sorting. Every word earns its place.

Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.

Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?

Given the simple nature of the tool (1 param, no output schema), the description covers the main purpose and key behaviors. However, it lacks explanation of the parameter semantics and return format (what the buy links contain). Adequate but with clear 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 description coverage is 0% for the single parameter event_id. The description does not explain what event_id is, its format, or how to obtain it (e.g., from get_event). This is a critical gap as the agent has no guidance on providing a valid input.

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 compares ticket prices across multiple named marketplaces and returns buy links sorted cheapest first. This specific verb+resource combination distinguishes it from siblings like get_event (event details) or search_events (event discovery).

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 ticket prices across marketplaces but does not explicitly state when to use it versus alternatives, nor does it mention when not to use it or any prerequisites. Usage is clear from context but lacks explicit 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 carries the burden of explaining behavior. It discloses what the response includes (provenance fields and venue facilities), which is useful, but it does not describe error behavior, missing-event responses, or whether any authentication/restrictions apply. The description adds meaningful output detail but leaves some behavioral gaps.

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 conveys the tool's purpose, input source, and key output fields without wasted words. The parenthetical listing of provenance subfields is compact and immediately relevant.

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 single-id fetch tool with no output schema, the description covers the critical details: what to pass in and what to expect back. It does not discuss error responses or edge cases, but the core information needed to call the tool correctly 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 coverage is 0%, so the description must compensate. It does so by explaining that the id should come from search_events results, adding important semantic context beyond the bare string type. For a single parameter, this is sufficient 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?

The description uses a specific verb ('Get full details') and identifies the exact resource ('one event by its id'). It also names the source of the id ('from search_events results') and lists key returned fields, making the tool's purpose distinct from siblings like get_event_history 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 phrase 'from search_events results' provides clear context on how to obtain the required id and implies that search_events should be used first. It does not explicitly state when not to use this tool or name alternatives like get_event_history, but the usage context is clear enough.

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

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

Discloses key behavioral traits: change-only recording (not full snapshots), a start date, and that points are recorded only on change. This goes beyond a generic 'get history' and helps the agent understand data granularity. However, it doesn't mention response format or pagination, but with no annotations, this is solid coverage.

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 crisp sentences: the first defines what it returns and its scope; the second gives concrete use cases representation. No wasted words, fully 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 simple one-parameter read tool without an output schema, the description conveys the nature of the data returned and its boundaries (start date, change-only). It could mention the return shape or ordering, but the core semantics are sufficient for an agent to decide and call 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 sole parameter event_id is self-explanatory given the tool name, so the description adds no extra semantics. Since schema coverage is 0% (no description field), the description could have noted the expected format, but for a single obvious string ID, the absence 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 states the tool retrieves change-only history for an event, citing specific data points (price moves, sold-out, date shifts, delisting) and its temporal scope. It distinguishes itself from sibling tools like get_event (which likely provides current state) and search_events (discovery) by focusing on temporal changes.

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?

Provides concrete example questions the tool answers ('is the price going up or down', 'was this rescheduled'), making it clear when to use it. It doesn't explicitly state when not to use it or name alternatives, but the examples effectively scope its use 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?

With no annotations, the description carries the burden. 'List all' clearly conveys a read-only, non-destructive operation. It enriches context by explaining that the output represents the controlled vocabulary shared across sibling tools, which is useful behavioral 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?

A single, well-structured sentence that immediately states the action (List), the object (all taxonomy tags), and the purpose (used by other tools). No filler or 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 parameterless list tool, the description covers the essential scope (types of categories) and the relationship to sibling tools. It could mention the output format (e.g., grouped list or labels), but that is not critical given the low complexity.

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 baseline is 4. There is no schema information to clarify, and the description appropriately does not attempt to invent parameter details.

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 'List' and identifies the exact resource: all event category tags, bar/club vibe tags, kid-place categories, and museum types. It clearly distinguishes from sibling tools by positioning it as the shared taxonomy reference for the other 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?

It states the taxonomy is 'used by the other tools', implying it is a reference for obtaining valid tag values before using find_bars, find_museums, or search_events. This gives clear context, though it does not explicitly name alternatives or provide exclusion criteria.

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 a strong job: it explains the composite `rating` (1-13) and `popularity` metrics, the `provenance` block (source, last_seen_at, removal/cancellation status, price_as_of, confidence), and the `market` block for market events. It also discloses that market events have paid-at-the-gate entry and that low-confidence events should be verified. This is rich behavioral context beyond what the schema provides.

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 dense but well-structured, front-loading the core purpose and then layering details about ratings, badges, markets, and provenance. Every sentence adds information. It is longer than ideal, but given the tool's complexity (12 parameters, multiple special cases), the length is justified. The structure flows logically from general to specific.

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 tool with 12 parameters, no output schema, and no annotations, the description is remarkably complete. It covers the return format (compact JSON with venue, date, price-from, artists, tickets link), the special `market` block, the `provenance` block, and the confidence guidance. The only missing piece is explicit guidance on how to combine filters (e.g., can query and tags be used together?), but that is a minor gap given the overall richness.

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 83%, so the schema already documents most parameters well. The description adds value by explaining the semantics of `rating` (10+ = major act, 11+ = megastar) and `popularity` (headliner fan count, charting status, ticket site listings), which are not in the schema. It also clarifies the `badge` enum values (e.g., freeday = museum free-admission day, not a nightclub promo). The only minor gap is that `max_price_usd` and `free_only` are not described in the description, but the schema covers them.

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 ('Search upcoming Los Angeles events') and immediately enumerates the categories covered (concerts, parties, theater, family, sport and more). It distinguishes itself from siblings by naming the `#markets` tag as a category absent from every ticketing feed, and by describing the `badge` filter as covering audiences no other catalogue indexes. This clearly differentiates it from find_bars, find_museums, and whats_on_tonight.

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 clear context on when to use the tool: for searching events with filters, and it explicitly calls out the `#markets` tag for flea/vintage/night markets that are absent from ticketing feeds. It also tells the agent to prefer high-confidence events and to tell users to verify low-confidence ones. However, it does not explicitly state when NOT to use this tool versus siblings like find_bars or find_museums, though the category list implies those are covered here too.

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 provided, so the description must carry the burden. It discloses time filters (events until ~6am, bars with happy hour now) but fails to mention output behavior, location dependence, or lack of results handling.

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 no fluff, but its brevity sacrifices completeness, particularly regarding parameters.

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 tool with 2 unannotated parameters and no output schema, the description lacks essential details about inputs, output format, and edge cases, making it insufficient for reliable agent invocation.

Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.

Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?

Schema description coverage is 0% and the description does not mention the lat/lon parameters at all, leaving their meaning and usage completely unspecified.

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 a combined list of top events and bars for tonight in LA, distinguishing it from sibling tools like find_bars 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 description implies usage for tonight's combined events and bars, but does not explicitly state when to avoid this tool or mention alternatives beyond the implicit sibling distinction.

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