UPFRONT

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

Annotations already declare readOnlyHint and idempotentHint, but the description adds materially important caveats: fromAllInPrice is the last-checked page price, not a per-ticket-count quote, and null means no tickets listed rather than sold out. It also discloses ordering (soonest first), the 60-item cap, totalDates semantics, and the found/otherMatches disambiguation states.

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?

Front-loads the answer scope and example queries before parameter docs and return-field docs, so an agent can stop reading early. It is on the long side with some repetition around price semantics, but nearly every sentence adds a distinct constraint rather than restating the schema.

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 fully specifies the response shape (eventId, name, venue, city, state, local start time, fromAllInPrice, url) plus the auxiliary fields tourGuide, artistPage, otherMatches and found. For an 8-parameter, annotation-light return surface, nothing an agent needs to call or interpret the result is missing.

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

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

Schema description coverage is 0%, so the description carries the full burden and does: artist format with an example, near as a place name with radius default 150, lat/lng as a shared-location alternative, inclusive YYYY-MM-DD date bounds, and limit default 30 / max 60. Every one of the 8 parameters is given meaning the schema does not.

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?

Opens with a specific verb-and-resource statement ('An artist's upcoming dates on UPFRONT') plus the exact payload returned: dates, venues, cities, lowest all-in price per show, and tour guide. The three sample queries pin the resource and scope tightly enough to separate it from generic event search siblings like find_events or compare_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?

Gives concrete trigger phrasings ('when is Olivia Rodrigo playing near Philadelphia?', 'cheapest Iron Maiden show this spring') and notes it handles shows across several cities, so an agent can pattern-match intent. It stops short of explicitly naming the siblings it supersedes (find_events, compare_prices) or stating when-not-to-use, which keeps it off a 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?

Beyond the readOnly/idempotent/openWorld annotations, the description discloses real behavioral edge cases: 'placed' limits which listings are ranked, hasFacts=false degrades ranking to price-only, and {"error": "search_failed"} is a lookup failure, not an empty inventory. Those are exactly the surprises an agent needs warned about.

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?

Front-loaded with the differentiator ('ranked by where the seats are, not only by price') followed by parameter and return-value blocks; almost every sentence earns its place. It is dense and long, with some run-on enumeration in the return-value paragraph, but nothing is padding.

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 description compensates by enumerating the returned fields (section, row, all-in price, distance, height, covered, 3D view picture, section page, reason, buy url) and by explaining degraded and error states. An agent can call and interpret results without further documentation.

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

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

Schema description coverage is 0% and the schema only gives bare titles, so the description carries the full burden — and it does: prefer is defined with all four accepted values plus the default, quantity is 'tickets needed together', max_price is per-ticket with fees, max_total_price is group budget, limit is 5 default / 10 max, and event_id is tied to eventId from event results.

Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.

Does the description clearly state what the tool does and how it differs from similar tools?

The description states a specific verb and resource: it ranks UPFRONT's own listings for one event by seat location rather than price alone, illustrated with concrete query examples. It implicitly separates itself from compare_prices ('not only by price', 'UPFRONT's own listings') but never names that sibling, so an agent must infer the boundary.

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 opening examples ('best view for 2 under $250', 'seats in the shade') make the intended use case concrete, and the prefer values clarify which framing to choose. It stops short of explicit alternatives or exclusion rules (e.g., when to call compare_prices or find_ticket_groups instead).

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 goes well beyond the readOnly/idempotent annotations by explaining site statuses (ok/incomplete/failed), otherSites freshness, unavailable/notShown distinctions, and error meanings such as search_failed and event_not_found. This is exactly the behavioral context an agent needs to interpret results correctly.

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 well structured and front-loads the core purpose before parameters and return details. It is somewhat long, especially the return-value section, but the density of useful semantics justifies most of the length.

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 six-parameter, multi-site price comparison tool with no schema descriptions, the description is exceptionally complete. It covers parameter meaning, return shape, freshness caveats, and error/status semantics.

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?

Despite 0% schema description coverage, the description explains every parameter: event_id, quantity, max_price, max_total_price, section, and limit. It adds crucial semantics, especially the distinction between per-ticket max_price and group-level max_total_price.

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 that this tool returns one event's all-in prices across UPFRONT and other marketplaces, cheapest first. It is specific about the resource and operation, though it does not explicitly distinguish itself from sibling tools such as best_seats or find_ticket_groups.

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 provides concrete example requests ('cheapest 2 seats together...', 'who has it cheaper?') that clearly indicate when to use the tool. It stops short of naming alternatives or stating when not to use it, but the context is strong.

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?

Goes well past the readOnly/idempotent annotations: discloses sort order, that parking/tailgate events are appended after games, that an empty list can mean the search failed, and that the catalog carries no audience/vibe/popularity signals. It also explains price semantics (fees included, latest check, not a quote, null means none listed).

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?

Front-loads purpose and sort order, then walks parameters in a logical order. Slightly long with repeated illustrative examples, but the density is warranted by 12 undocumented parameters; nothing is truly redundant.

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?

No output schema exists, yet the description enumerates every returned field (eventId, name, venue, city, state, startsAt, fromAllInPrice, url) and explains the non-obvious ones plus the echoed place/performer and otherMatches. Minor gaps like page size and lat/lng interplay with radius don't impede 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?

With 0% schema description coverage across 12 parameters, the description does the entire job: it defines query vs performer vs near, spells out the full category enum and warns there is no generic 'concerts' value, maps natural-language dates ('this weekend', 'tonight') to ISO ranges, and clarifies max_price includes fees and radius defaults to 100 km.

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?

Opens with a precise verb+resource+scope: events on sale on UPFRONT across games, concerts, shows and comedy, sorted soonest first. The example queries anchor what it matches, and none of the siblings (best_seats, compare_prices, venue_info, etc.) overlap with catalog 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?

Gives concrete request patterns and comparative guidance ('performer is more exact than query for a team or artist'), plus default behavior ('*' means no text filter; without dates every upcoming date counts). It stops short of explicitly naming when to hand off to siblings like best_seats or compare_prices.

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 read-only, idempotent, non-destructive, and closed-world behavior. The description adds substantial context: grouping semantics for quantity, fee-inclusive pricing, error behavior, parking exclusion, and view/location signals available on listings.

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 long but appropriately sized for 11 parameters and nuanced return behavior. It is front-loaded with purpose and sorting behavior, then organized into parameter and return-value sections with no 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?

Despite a separate output schema, the description adds important operational context about return values, error semantics, parking handling, and view information. Everything an agent needs to invoke and interpret the tool 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 0%, so the description carries the full burden and does so thoroughly. It explains every parameter's meaning, units, dependencies, grouping constraints, and defaults, including that max_total_price requires quantity.

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 specifically that this returns UPFRONT's own listings for one event, sorted cheapest first, and explicitly says it does not search by name or place. This distinguishes it from find_events and the other sibling 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?

Explains that event_id comes from event results and that this tool cannot be used to search by name or place. It also clarifies the parking-only exception and how to interpret an empty result versus a search_failed error.

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?

Although annotations already cover the safety profile (readOnly, idempotent, non-destructive, closed-world), the description adds important context about outcomes that annotations cannot express. It lists three distinct return shapes: {"mapImageUrl"} on success, {} when the venue has no chart, and {"error": "search_failed"} after retries fail. It also explicitly states that the last two cases mean no map is available, so the agent can handle them correctly.

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 front-loaded: it defines the artifact in the first sentence, separates the parameter explanation with a blank line, and separates the return contract as well. Every sentence adds non-redundant information, and there is no filler.

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

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

For a one-parameter, read-only lookup with no output schema, everything needed is present: what the tool returns, its failure modes, the provenance of the single argument, and the caveat that the image is not date-specific. There are no obvious 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%, so the description has to explain the only parameter, and it does: event_id is UPFRONT's event id and appears as eventId in event results. That provenance detail is exactly what an agent needs to supply a valid integer, and it adds real value beyond the bare 'Event Id' schema title.

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?

It names a specific resource and format: the URL of a venue's seating-chart image for one event, so the agent knows it returns an image link, not seat data. It also clarifies that the image is tied to the venue layout and not to a specific date. It does not explicitly distinguish itself from siblings such as section_info or venue_info, which might be confused with the image 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 gives a concrete signal for when this call works: the chart belongs to the venue layout, so it is safe to call even for far-future events with no tickets yet. It does not say when to prefer a sibling tool, such as section_info for textual section details or best_seats for recommendations, and it names no explicit exclusions.

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

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

Adds substantial context beyond annotations: 'Nothing on UPFRONT changes until a person checks it: the correction is passed on, not fixed' explains the human-review pipeline, and 'Every request files a separate report' explains the non-idempotent behavior flagged by idempotentHint=false. It also discloses the 1,000-character claim limit.

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?

Front-loads the action and a one-line usage rule, then uses a compact param glossary. Every sentence earns its place — the example corrections and the enum list are necessary because the schema provides neither.

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?

Covers what the tool does, when to use it, all six parameters, the length constraint, and the downstream consequence (human review, one report per call). No output schema exists, and the description explains the outcome well enough that one isn't needed.

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%, so the description carries the full burden and does: it enumerates every allowed value of 'kind' (rows, view, section, bags, entry, parking, transit, food, other), describes venue/section/row scoping, the claim content and length cap, and that source is optional provenance.

Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.

Does the description clearly state what the tool does and how it differs from similar tools?

States a specific action and resource — passing a fan's correction of an UPFRONT fact to a human reviewer — with three concrete examples ('section 112 has 18 rows, not 17'). It is clearly the only write/report tool among read-only siblings like section_info and venue_info.

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 is for corrections the user gives or asks to send to UPFRONT' gives a clear triggering condition. It does not explicitly name a sibling to use instead when the user just wants to read a fact, but the read/write distinction is implicit and unmistakable.

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 readOnly, idempotent, non-destructive, so the safety profile is covered. The description adds genuinely non-obvious behavior: coverage is limited to UPFRONT-chart venues, missing row data between front and back is interpolated and flagged 'estimated', and viewPicture is a 3D render rather than a photo.

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?

Front-loaded with the capability and example questions, then parameter definitions, then caveats. Every block earns its place, though the trailing paragraph explaining viewPicture/sectionPage/source adds length that could be tightened slightly.

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?

No output schema exists, and the description compensates by naming the return fields (viewPicture, sectionPage, source) and explaining what viewPicture actually is. Coverage limits and the estimated-value flag round out what an agent needs to call and interpret this 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 0%, so the description must carry the load and it does: venue accepts names and abbreviations ('MSG'), section accepts number or ticket-style name ('Field Box 22'), and row is optional with a documented estimation behavior for interior rows.

Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.

Does the description clearly state what the tool does and how it differs from similar tools?

States a specific verb-and-resource combination ('What one section of a venue is like') and enumerates the exact facts returned: location, distance from the field/court/stage, height, covered rows, and a view picture. The example questions ('is section 314 at Lumen Field covered?') make the boundary against venue_info and get_seating_map obvious.

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 triggering questions and two explicit scope exclusions: it has no row or seat counts, and it only covers venues with an UPFRONT seating chart (others return a message listing covered venues). It stops short of naming a sibling to use instead when the venue isn't covered or when row/seat counts are needed, so it isn't a full when/when-not/alternatives statement.

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 establish read-only, idempotent, non-destructive behavior, and the description adds useful context beyond them: answers are the guide's own words, include FAQ text, factsLastChecked freshness, guideUrl, and caveats that rules can change per event and the venue's own page is final. It also clarifies dietaryNotMentioned semantics, which is important for correct interpretation.

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 front-loaded with purpose, then examples, parameter definitions, coverage limits, and return-field semantics. Despite being detailed, every section serves a clear function for a guide-lookup tool with several caveats.

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?

No output schema exists, so the description explains return content sufficiently: guide text, FAQ questions and answers, factsLastChecked, guideUrl, dietary lists, and dietaryNotMentioned. It also covers venue coverage limits and freshness caveats, which are essential for correct use.

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%, so the description must carry parameter meaning. It does: venue is the venue's name with examples like "MetLife Stadium" and "MSG"; topics is optional and accepts only bags, entry, parking, transit, or food, with all topics returned when omitted.

Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.

Does the description clearly state what the tool does and how it differs from similar tools?

The description states a specific resource and scope: what fans need to know before going to a venue, including bag policy, entry, parking, transit, food and drink, and dietary options. It clearly distinguishes this guide-style lookup from sibling ticket/seating/tour tools by naming the exact questions it answers.

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 says when to use it via example questions and defines coverage limits: only the 36 venues with UPFRONT guides, while other venues return a message listing covered venues. It also explains how the optional topics parameter narrows the answer versus returning every part.

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