Change-only history of what the source said about an event over time: price moves, sold-out, date shifts, delisting/cancellation, with when we first saw the listing. Use it to answer "is the price going up or down", "was this rescheduled" or "has this been removed". History starts 2026-08-22; points are recorded only when something changed.
| Name | Required | Description | Default |
|---|---|---|---|
| event_id | Yes |
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.
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Each tool has a clearly distinct role: venue finders (bars, museums), event search, ticket price comparison, event details, change history, taxonomy, and a curated nightly snapshot. While find_bars and search_events could theoretically overlap for nightlife, the descriptions explicitly scope them to venues vs. events, eliminating ambiguity.
The tool names mostly follow a verb_noun pattern: find_* for venue discovery, get_* for retrieval, search_events for event search. However, `whats_on_tonight` breaks the pattern with an idiomatic phrase, and `find` vs `search` are used interchangeably for discovery, causing minor inconsistency.
With 8 tools, the server is well-scoped for an LA event discovery service covering search, venue lookups, ticket prices, history, taxonomy, and a nightly roundup. Each tool earns its place without redundancy or bloat.
The tool surface covers the full read-only lifecycle: searching events, finding bars and museums, retrieving event details, comparing ticket prices, viewing historical changes, and exploring taxonomy tags. For its stated purpose of providing a synoptic LA event view, there are no significant gaps.