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Glama

Ticketmaster

by io.github.pipeworx-io

Resolve Entity

resolve_entity
Read-onlyIdempotent

"What's the ticker for…" / "find the CIK for…" / "what's the LEI for…" / "what's the RxCUI for…" / "look up the ID for…" / "what is X's official identifier" / "who owns X" / "is X a subsidiary of Y" — resolve a user-spoken NAME to the canonical/official identifiers other tools require as input. Use FIRST whenever you have a name but need an ID. SUPPORTED TYPES: "company" (cross-source identity spine: 10-digit CIK + ticker + company_name from SEC EDGAR, legal-entity LEI from GLEIF with parent/ultimate-parent/children ownership when the LEI resolves, and security FIGI from OpenFIGI — by exact ticker map when a ticker is implied, and otherwise by name search, so NON-EQUITY instruments that never have a ticker (municipal and corporate bonds, notes, authority debt) DO resolve here; when a name matches more than one instrument it asserts nothing and returns figi_candidates to pick from, which is the correct answer to an issuer name that does not identify a single bond; every identifier is labelled with the source that established it, and an identifier that could NOT be resolved is stated explicitly under unresolved rather than omitted — accepts ticker, CIK, ISIN, or company name as input; an ISIN like "CH0038863350" resolves to the LEGAL ENTITY that issued the security via the GLEIF ISIN-to-LEI mapping, covering non-US issuers EDGAR cannot reach), "drug" (returns RxCUI + ingredient + brand from RxNorm + pipeworx://rxnorm/concept/{rxcui} citation; accepts brand or generic name). LEI/FIGI enrichment degrades gracefully — if GLEIF or OpenFIGI is unavailable, the EDGAR identifiers still return. Each call cascades through several lookup endpoints internally — using resolve_entity replaces 2-3 manual lookups.

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
typeYesEntity type: "company" or "drug".
valueYesFor company: ticker (AAPL), CIK (0000320193), or name. For drug: brand or generic name (e.g., "ozempic", "metformin"). Pass the ENTITY NAME ONLY — for a bond that is the ISSUER exactly as printed ("NEW YORK ST DORM AUTH"), never the question's full noun phrase ("NEW YORK ST DORM AUTH revenue bonds"): the FIGI lookup matches instrument names, so trailing security-class words match nothing.

Schema Changelog

Changes observed during successful MCP inspections. Dates show when Glama detected each change.

  1. Changed1 schema field changed
    • changedInput schema / properties / value / description
      Previous value: -"For company: ticker (AAPL), CIK (0000320193), or name. For drug: brand or generic name (e.g., \"ozempic\", \"metformin\")."New value: +"For company: ticker (AAPL), CIK (0000320193), or name. For drug: brand or generic name (e.g., \"ozempic\", \"metformin\"). Pass the ENTITY NAME ONLY — for a bond that is the ISSUER exactly as printed (\"NEW YORK ST DORM AUTH\"), never the question's full noun phrase (\"NEW YORK ST DORM AUTH revenue bonds\"): the FIGI lookup matches instrument names, so trailing security-class words match nothing."
  2. First observed

TDQS

A4.7/5.0
Behavior5/5

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

Beyond the readOnly/idempotent annotations, it details graceful degradation if GLEIF or OpenFIGI are unavailable, returns figi_candidates when ambiguous, exposes unresolved identifiers explicitly, and labels each identifier by source. It also discloses that each call cascades through several internal lookup endpoints.

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?

The description is front-loaded with examples and the Use FIRST directive, and nearly every sentence adds information. However, the company explanation is a single dense parenthetical block that would be easier to navigate if broken into bullets, so it loses one point for structure.

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?

Despite having no output schema, the description explains expected return fields and edge-case behavior for both entity types, including figi_candidates and unresolved. It covers ambiguity, source labelling, ISIN-to-LEI resolution, and degraded enrichment, which is enough for an agent to know what the tool returns.

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

Parameters5/5

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

Schema coverage is 100%, so the baseline is 3, but the description adds crucial semantics: accepted input forms for company (ticker, CIK, ISIN, or name) and drug, the distinction between issuer name and security name, and the warning to avoid trailing security-class words. This materially improves the agent's ability to construct valid calls.

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?

The description opens with concrete natural-language examples and then states a specific action: resolve a user-spoken NAME to canonical/official identifiers. It positions the tool as the ID-resolution entry point for other tools, which differentiates it from siblings like entity_profile or compare_entities.

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

Usage Guidelines4/5

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

It gives an explicit selection rule: Use FIRST whenever you have a name but need an ID, plus examples of ownership and subsidiary queries that map to LEI enrichment. It does not name alternative tools or state when not to use it, so it stops short of a 5.

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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TDQS

C2.9/5.0
Disambiguation2/5

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 Consistency3/5

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.

Tool Count2/5

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.

Completeness4/5

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.

Resources