Skip to main content
Glama

Studio Ghibli

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

A4.6/5.0
Behavior5/5

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

Annotations already declare readOnlyHint, openWorldHint, and idempotentHint. The description adds extensive behavioral context beyond that: it explains multi-source lookup cascading (SEC EDGAR, GLEIF, OpenFIGI, RxNorm), how ambiguous matches are handled (returning figi_candidates), explicit reporting of unresolved identifiers, ISIN-to-LEI mapping, and graceful degradation of enrichment. This is a wealth of non-obvious behavior that annotations cannot convey.

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 but densely packed with unique, useful information. It is organized with clear markers (SUPPORTED TYPES) and front-loaded with the core purpose. While every sentence earns its place, there is some redundancy with schema content (e.g., repeating that type accepts 'company' or 'drug'), which keeps it from being perfectly concise.

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?

For a tool with two complex entity types, multiple data sources, and edge cases, the description is remarkably complete. It covers input formats, output behavior (candidates, unresolved list), exceptions (non-equity instruments, non-US issuers), and even performance notes (replaces 2-3 lookups). With no output schema, these details are essential and fully provided.

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?

The schema already has 100% parameter description coverage, giving baseline 3. The description adds significant value beyond the schema: it explains that ISINs resolve to legal entities via GLEIF, gives a specific caveat for bond issuer names ('Pass the ENTITY NAME ONLY... trailing security-class words match nothing'), and enumerates accepted input forms for each type. This enriches parameter understanding meaningfully.

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 states a specific verb ('resolve') and resource ('user-spoken NAME to canonical/official identifiers'), and clearly distinguishes itself from sibling tools by focusing on converting names to IDs. It also specifies supported types ('company', 'drug') and data sources, making it unmistakable what the tool does.

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 description explicitly says 'Use FIRST whenever you have a name but need an ID', which is a clear directive. It also provides detailed conditions for when resolution is correct (e.g., ambiguous names yield candidates) and notes degradation behavior. However, it does not explicitly say when *not* to use it (e.g., when an ID is already available), so it stops short of a perfect 5.

Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.

All Studio Ghibli toolsInspect
Try in Browser
RayrunBuild, deploy, and operate MCP servers without becoming an infrastructure team.ray.runAd

Glama MCP Gateway

Add one secure layer between your agents and this server.

TDQS

B3.3/5.0
Disambiguation1/5

The set contains multiple near-identical query tools (ask_pipeworx, ask_pipeworx_beta, ask_pipeworx_grounded, deep_research) and five overlapping prediction-market tools (polymarket_arbitrage, polymarket_edges, polymarket_edge_tracker, polymarket_fill_risk, polymarket_kalshi_spread) that an agent could easily confuse. The Studio Ghibli tools are clear enough, but they are drowned out by a large unrelated cluster with fuzzy boundaries.

Naming Consistency2/5

There are small internally consistent clusters (polymarket_* tools, ask_pipeworx variants, singular/plural Ghibli resource pairs), but the overall set mixes simple nouns (film, person, location), imperative verbs (remember, forget, recall), and descriptive compound names (ai_visibility_check, generate_llms_txt, scan_competitor_ai_presence). The species tool is 'species'/'species_one' while every other resource uses bare singular for the single-item fetch, breaking the otherwise predictable Ghibli pattern.

Tool Count1/5

A server named 'Studio Ghibli' exposes 41 tools, but only 10 of them relate to Ghibli content; the other 31 are an unrelated general-purpose data, prediction-market, memory, and subscription toolkit. This is an extreme mismatch between the apparent purpose and the actual tool surface.

Completeness4/5

For the Ghibli data domain itself, the surface is solid: films, people, locations, vehicles, and species all have list and single-item lookup, plus cross-links between entities. Minor gaps exist, such as no search or filter capability and no way to fetch films by director or year, but the core read-only catalog is well covered.

Resources