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Glama

Ticker: Live Event Ticket Market

Event Bundle

get_event_bundle
Read-only

One event's price series, current analytics and sections in one call, as a short summary: the headline prices, listings and demand, where the series started, ended, bottomed and peaked, and the top sections now. Pass rows true for each part's rows. Use it when you need more than one of these on one event; get_event_chart, get_event_analytics and get_event_sections read one part in full.

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
rowsNoFalse (default): the summary. True: the full analytics row, each date of the series and each section's prices with its last 7 days, each part cut to fit with a pointer to read on.
partsNoWhich parts to read: analytics, chart, sections. Omit for all three.
rangeNoHow far back the price series reaches. Default 28d; all is about a year.28d
event_idYesThe event to read. Ticker event id, `ev` plus ten characters, as every tool here returns it.

Schema Changelog

Changes observed during successful MCP inspections.

  1. Added

TDQS

A4.3/5.0
Behavior4/5

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

Annotations already cover the safety profile (readOnly, non-destructive, closed world), so the description is free to add what they can't: it discloses that output is a summary by default and that rows=true yields truncated data 'cut to fit with a pointer to read on'. That truncation/pointer behavior is genuinely useful beyond the structured fields, though it says nothing about permissions or rate limits.

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?

Two sentences, front-loaded with the combined scope and followed by the routing rule; the middle enumeration of summary contents is dense but earns its place. Slightly list-heavy, but no filler.

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?

There is no output schema, and the description does sketch what comes back (headline prices, listings, demand, series start/end/bottom/peak, top sections) plus the summary-vs-rows tradeoff, which is adequate. The full row shape still depends on the schema text, so it stops short of complete.

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

Parameters3/5

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

Schema description coverage is 100%, so the schema already defines rows, parts, range and event_id in detail; baseline is 3. The description adds only a light restatement ('Pass rows true for each part's rows') and never mentions the range or parts parameters, so it does not meaningfully exceed 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 (read one event's price series, analytics and sections in a single call) and explicitly contrasts itself with the three sibling tools that read one part in full. An agent can distinguish it from get_event_chart/analytics/sections without opening any 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 an explicit selection rule: 'Use it when you need more than one of these on one event', then names the alternatives and their condition ('read one part in full'). No inference required.

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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