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Glama

Artsmia

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

A4.4/5.0
Behavior5/5

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

Annotations already mark it read-only and idempotent; the description adds substantial behavioral detail: internal cascading through several lookup endpoints, graceful degradation when GLEIF/OpenFIGI are unavailable, and coverage of non-equity instruments and non-US issuers through ISIN. It also describes the drug citation format returned.

Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.

Conciseness3/5

Is the description appropriately sized, front-loaded, and free of redundancy?

The description is dense and informative, but it contains a duplicated clause about non-equity instruments resolving, which is redundant. It is a long single block rather than organized, though the front-loaded query examples help.

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?

With no output schema, the description explains return values (CIK, ticker, LEI, FIGI, RxCUI, brand, citation) and failure degradation for unavailable sources. It does not describe behavior for ambiguous or unresolvable names, a small but meaningful gap.

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 enriches the value parameter with important context: accepted inputs include ISIN, and the entity-name-only rule prevents failed FIGI lookups on bonds. This goes beyond the schema's terse type descriptions.

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 names a specific operation — resolving a user-spoken name to canonical/official identifiers — and lists the exact identifier types returned for both company and drug. It also gives concrete triggering queries ('find the CIK for…'), making its purpose unmistakable and distinct 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?

The instruction 'Use FIRST whenever you have a name but need an ID' provides a clear context for when to invoke the tool, and the value parameter guidance warns against sending full noun phrases. It does not explicitly name alternative tools or state when not to use it, so it falls just 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

A3.6/5.0
Disambiguation2/5

Several tools have heavily overlapping purposes: ask_pipeworx and ask_pipeworx_beta are described as currently identical, and the polymarket_* family (edges, arbitrage, edge_tracker, fill_risk, kalshi_spread) all target prediction-market analysis with fuzzy boundaries. ai_visibility_check and scan_competitor_ai_presence overlap as well. While some tools are clearly distinct (art search vs memory), the set as a whole requires careful reading to avoid misselection.

Naming Consistency2/5

Tool names follow multiple patterns: verb_noun (search_artworks, resolve_entity, validate_claim), domain_prefixed (polymarket_*, pipeworx_*), and product-style names (ask_pipeworx, bet_research, deep_research). Versioned suffixes like ask_pipeworx_beta and ask_pipeworx_grounded break any unified convention. Even though subgroups are internally consistent, the overall pattern is mixed and unpredictable.

Tool Count2/5

34 tools is on the high side, especially for a server named after an art museum. Only 3 tools actually relate to the Minneapolis Institute of Art, while the rest are a general-purpose data platform, prediction-market analysis, and memory/subscription features. Many of these extra tools are redundant or power-user variations, making the count feel inflated relative to the apparent domain.

Completeness3/5

For the stated art domain, the read-only surface (search, get, department highlights) is functional but thin — no artist browse, exhibitions, or advanced filtering. The broader data tools are comprehensive in themselves, but their presence distracts from the core domain and creates confusion about the server's intended purpose. The art coverage is adequate for basic queries but lacks depth expected from a dedicated museum collection API.

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