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Ticker: Live Event Ticket Market

List Screener Columns

list_screen_columns
Read-only

Lists every column a screen_events, create_view or update_view predicate can name, with its meaning, unit and the plan that includes it; a venue's name is v.venue_name. Pass search with every word you need in one call (price, demand, inventory, rank, date, category) for only the matching columns. Call it before you write a predicate; column names must be used exactly. Use get_event_analytics for the values on one event.

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
offsetNoSkip this many columns. The catalog comes back in parts: a cut answer's meta.next gives the offset for the next part.
searchNoOne or more words, e.g. "price demand": only the columns whose name, group or meaning contains any of them. Omit for the whole catalog.

Schema Changelog

Changes observed during successful MCP inspections.

  1. Changed1 schema field changed
    • addedInput schema / properties / search
      Added value: +{
      +  "description": "One or more words, e.g. \"price demand\": only the columns whose name, group or meaning contains any of them. Omit for the whole catalog.",
      +  "type": "string"
      +}
  2. Changed1 schema field changed
    • addedInput schema / properties / offset
      Added value: +{
      +  "description": "Skip this many columns. The catalog comes back in parts: a cut answer's meta.next gives the offset for the next part.",
      +  "minimum": 0,
      +  "type": "integer"
      +}
  3. First observed

TDQS

A4.6/5.0
Behavior4/5

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

Annotations already cover the safety profile (readOnlyHint=true, destructiveHint=false, openWorldHint=false), so the description's job is to add what they don't: that a 'venue's name is v.venue_name' (a concrete naming gotcha), and that multiple search words should be passed together in one call rather than looped. It does not discuss result volume or rate limits, which is the remaining gap.

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?

Dense but front-loaded: the resource and payload come first, then the search tactic, then the precondition and the alternative. The semicolon chain packs a lot into few sentences with no wasted clauses, though the density borders on cramped.

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

Completeness5/5

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

With no output schema, the description still describes the return contents (meaning, unit, plan) and the description/schema pair covers pagination via the offset parameter and meta.next. For a two-parameter read-only lookup tool, nothing an agent needs to call it correctly is missing.

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 baseline is 3. The description still adds value by naming candidate search terms (price, demand, inventory, rank, date, category) and by directing that all needed words go into a single call instead of being issued separately, which the schema's 'any of them' wording does not make obvious.

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 (Lists) and resource (every column a screen_events, create_view or update_view predicate can name), plus the return payload (meaning, unit, plan). It anchors itself to the exact sibling tools whose predicates consume these columns, so an agent can place it 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?

Explicit precondition: 'Call it before you write a predicate; column names must be used exactly.' Explicit alternative: 'Use get_event_analytics for the values on one event.' Both the when-to-use and the when-to-use-something-else are stated.

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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