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Glama

Thegamesdb

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?

The description goes well beyond the readOnly/idempotent annotations: it documents ambiguity handling (`figi_candidates`), explicit `unresolved` output, graceful degradation when GLEIF/OpenFIGI are unavailable, and internal endpoint cascading. These traits are context an agent could not infer from annotations alone.

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 long and dense, but it is front-loaded with purpose, structured by entity type, and every major clause carries useful information. It slightly exceeds minimal conciseness due to heavy parentheticals, but this is justified by the tool's complexity.

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 thoroughly covers what the agent should expect: returned identifier fields, candidate selection on ambiguity, explicit unresolved fields, ownership data, and graceful degradation. An agent has enough context to call the tool correctly and interpret its results.

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?

Although schema coverage is 100%, the description adds substantial meaning: ticker/CIK/ISIN/name accepted for company, brand/generic accepted for drug, and a critical warning to pass only the issuer name for bonds, not the full noun phrase. This materially improves correct invocation beyond 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?

The description opens with concrete user phrasings ('What's the ticker for…', 'find the CIK for…') and states the core action: resolving a name to canonical/official identifiers. It explicitly contrasts itself with 'other tools require as input,' so an agent can distinguish it from sibling lookup tools like entity_profile or validate_claim.

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 a clear directive: 'Use FIRST whenever you have a name but need an ID.' It also explains that the tool replaces 2-3 manual lookups. However, it does not explicitly name alternative tools or state when NOT to use it, stopping short of a full 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

A3.6/5.0
Disambiguation2/5

ask_pipeworx and ask_pipeworx_beta are explicitly described as functionally identical right now, creating direct ambiguity. The six polymarket tools (edges, arbitrage, edge_tracker, fill_risk, kalshi_spread, bet_research) have blurred boundaries for prediction-market tasks, and ai_visibility_check vs scan_competitor_ai_presence is a wrapper relationship. Only the memory trio and subscription lifecycle are cleanly distinct.

Naming Consistency2/5

Naming follows multiple conventions with no global pattern: bare verbs (remember, recall, forget, subscribe), product-prefixed verbs (ask_pipeworx, pipeworx_feedback), noun phrases (entity_profile, deep_research, recent_changes), and verb_noun snake_case (discover_tools, validate_claim). There are consistent pockets (the polymarket_* family, the TheGamesDB get_/list_/search_ verbs), but the overall mix across 35 tools is inconsistent.

Tool Count2/5

35 tools exceeds the 25-tool threshold for a heavy server, and the count is wildly disproportionate to the server's stated identity: only 4 of 35 tools relate to TheGamesDB while 31 belong to an unrelated Pipeworx data/prediction-market suite. The game database would justify roughly 5-10 tools, so the bulk of this surface is out of scope for the server name.

Completeness3/5

The Pipeworx portion is genuinely thorough — discovery, grounded querying, entity resolution, claim validation, subscription lifecycle, memory, and feedback form a coherent coverage. However, the namesake TheGamesDB surface is thin: search, get-by-id, list genres, and list platforms, with no games-by-platform/genre browsing, no media/screenshots beyond front boxart, and no updates feed. The set as a whole covers multiple unrelated domains with no single complete lifecycle.

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