classificationsTicketmaster Discovery API — list all event classification segments, genres, and sub-genres (e.g. Music > Rock > Alternative). Use to discover valid classificationName values for filtering event_search.
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No arguments | |||
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Changes observed during successful MCP inspections. Dates show when Glama detected each change.
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
Annotations already establish read-only, idempotent, non-destructive behavior. The description adds context about the taxonomy hierarchy and the 'list all' scope, which goes beyond the schema. It doesn't discuss pagination or return format, but the safety profile is fully covered by annotations and an output schema exists.
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?
A single, well-structured sentence conveys the API source, the action, a concrete example, and the practical use case. Every phrase earns its place with no redundancy.
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 zero parameters, rich annotations, and an output schema, the description provides all necessary context for selecting and invoking the tool. It clearly explains the type of data returned and how it should be used in conjunction with event_search.
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 tool has zero parameters, so the input schema fully covers parameter semantics. The description reinforces that no input is needed by saying 'list all', and it provides useful guidance about classificationName values for downstream use with event_search.
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 uses a specific verb ('list all') with a clear resource ('event classification segments, genres, and sub-genres') and provides a concrete hierarchy example (Music > Rock > Alternative). This distinguishes it from the singular sibling 'classification' by emphasizing the plural, exhaustive listing behavior.
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 states when to use this tool: to discover valid classificationName values for filtering event_search. It doesn't name exclusions or directly contrast with the singular 'classification' tool, but the intended use case is clear and actionable.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
Add one secure layer between your agents and this server.
The tools fall into two unrelated domains (Ticketmaster event discovery and Pipeworx data research), and within the Pipeworx set there are near-duplicate tools like ask_pipeworx, ask_pipeworx_beta, and ask_pipeworx_grounded, plus multiple overlapping prediction-market tools (polymarket_edges, polymarket_arbitrage, polymarket_fill_risk, etc.). An agent would struggle to choose among these overlapping options and may not realize that most tools are unrelated to the server's stated name.
Naming uses consistent snake_case, but the pattern is mixed: Ticketmaster resource fetchers are bare nouns (event, venue, attraction, classification) while search tools use verb_noun (event_search, venue_search). Pipeworx tools vary between verb phrases (ask_pipeworx, validate_claim) and descriptive noun phrases (entity_profile, polymarket_kalshi_spread). This inconsistency makes predicting tool names harder, though each name is still readable.
41 tools is excessive for a server titled 'Ticketmaster' when only about 10 are Ticketmaster-related; the other 30 cover an entirely different service (Pipeworx). The count is far beyond a focused scope and suggests the server should be split into two separate, well-scoped MCP servers.
For the Ticketmaster half, the surface is complete for read-only event discovery (search events/venues/attractions, get single resources, classifications, autocomplete). For the Pipeworx half, the tool suite is extensive, covering lookup, research, prediction markets, memory, subscriptions, and feedback. The only notable gap is the lack of any write operations, but this is consistent with the read-only nature of the underlying APIs.