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Ticker: Live Event Ticket Market

Event Analytics

get_event_analytics
Read-only

Every number for one event as it reads now: prices, inventory, demand, their recent changes, prediction markets, and its presale and public on-sale windows. Pass range for how its numbers read on each past date, one row per date, to test a signal against what came next; never fill a date listed in days_missing. Use screen_events to compare many events, get_event_chart for the price series, research_event for why a number moved. Replaces get_event_analytics_history and get_event_sale_windows.

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
rangeNoLookback window over as_of_date: 7d | 14d | 28d | 90d | all (all is bounded to ~1 year). Default 90d.
columnsNoevent_analytics columns to return (bare names). Now: omit for the full row; `presales` and `onsale` name the sale windows. With range: omit for the headline signal set.
event_idYesThe event to look up. Ticker event id, `ev` plus ten characters, as returned by every tool here. A marketplace id also resolves when it carries its source prefix (`vs:`, `tm:`, `tp:`), and a bare marketplace id resolves until 2027-09-13.

Schema Changelog

Changes observed during successful MCP inspections.

  1. Changed2 schema fields changed
    • changedInput schema / properties / columns / description
      Previous value: -"Specific event_analytics columns to return (bare names, no namespace). Omit for the full row; this is the drill-down tool, so full is the default."New value: +"event_analytics columns to return (bare names). Now: omit for the full row; `presales` and `onsale` name the sale windows. With range: omit for the headline signal set."
    • addedInput schema / properties / range
      Added value: +{
      +  "description": "Lookback window over as_of_date: 7d | 14d | 28d | 90d | all (all is bounded to ~1 year). Default 90d.",
      +  "enum": [
      +    "7d",
      +    "14d",
      +    "28d",
      +    "90d",
      +    "all"
      +  ],
      +  "type": "string"
      +}
  2. Changed1 schema field changed
    • changedInput schema / properties / columns / description
      Previous value: -"Specific event_analytics columns to return (bare names, no namespace). Omit for the full row — this is the drill-down tool, so full is the default."New value: +"Specific event_analytics columns to return (bare names, no namespace). Omit for the full row; this is the drill-down tool, so full is the default."
  3. First observed

TDQS

A4.5/5.0
Behavior4/5

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

Annotations already establish readOnlyHint=true and destructiveHint=false, so safety is covered. The description adds genuine behavioral context beyond that: it discloses that a range call returns one row per date, that the tool now consolidates two former tools, and a do-not-fill constraint tied to days_missing. It stops short of describing pagination or output volume.

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

Conciseness4/5

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

Front-loaded with the core purpose, and each sentence carries routing or mode information rather than filler. It is dense and the 'never fill a date listed in days_missing' clause is cryptic since that field is never defined, which slightly hurts clarity without adding much length.

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

Completeness4/5

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

With no output schema, the description reasonably compensates by describing the return shape (full row vs. headline set, one row per date) and the sale-window fields. The gap is that days_missing is referenced as if defined elsewhere but is unexplained, leaving one behavioral rule ambiguous.

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

Parameters4/5

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

Schema coverage is 100%, so the baseline is 3; the schema already documents range, columns, and event_id. The description adds meaning beyond it by explaining that range yields one row per past date and by clarifying columns omission behavior (full row now vs. headline signal set with range), which is not stated in the schema.

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

Purpose5/5

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

States a specific verb+resource with enumerated scope ('prices, inventory, demand, their recent changes, prediction markets, and its presale and public on-sale windows'). It explicitly differentiates from siblings (screen_events, get_event_chart, research_event) and notes it replaces get_event_analytics_history and get_event_sale_windows, so an agent can place it precisely without opening a schema.

Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.

Usage Guidelines5/5

Does the description explain when to use this tool, when not to, or what alternatives exist?

Gives explicit routing: 'Use screen_events to compare many events, get_event_chart for the price series, research_event for why a number moved.' It also states the condition that selects history mode ('Pass range ... to test a signal against what came next') and a negative constraint ('never fill a date listed in days_missing').

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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