Skip to main content
Glama

Studio Ghibli

by io.github.pipeworx-io

Entity Profile

entity_profile
Read-onlyIdempotent

"Tell me about X" / "research Acme" / "brief me on Tesla" / "what does Apple do" / "company profile for Microsoft" / "give me the rundown on NVDA" / "everything you know about $TICKER" — full cross-source profile of a US public company in ONE parallel call. ALWAYS PREFER over chaining single-pack SEC/XBRL/news lookups when the user asks for a holistic view. Fans out across SEC EDGAR, XBRL, USPTO patents, federal contracts (USAspending), FDA-licensed biologics (Purple Book), H-1B hiring (DOL LCA), news and GLEIF, and returns: cik + company_name (+ resolved_from/resolved_to when value was a name); recent_filings (up to 5 with pipeworx://edgar/company/{cik}/filings/{accession} URIs); fundamentals (LATEST 10-K Revenues + NetIncomeLoss + Cash, sorted period_end DESC); patents (USPTO PatentsView API sunset May 2025 — soft-fails until reactivated); federal_contracts (USAspending awards where the company is the recipient); fda_products (FDA-licensed biologics — vaccines, cell/gene therapies — from the Purple Book; a company with only small-molecule/generic drugs will show none here, that is expected, not a failure); hiring (H-1B sponsorship volume + salary range from DOL LCA filings); recent news mentions via GDELT→GNews fallback; LEI via GLEIF. sources_used / sources_failed say which of these actually returned data for THIS company — an empty section is a real "no data", not a bug. Pass a ticker ("AAPL"), zero-padded CIK ("0000320193"), OR a company name ("Moderna") — names now resolve via SEC EDGAR's company-name match; a private company (no CIK/ticker) returns resolved:false with an explicit notes line, not a bare failure. type accepts "company" or "ticker" interchangeably — both take the same value shapes above.

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
typeYes"company" or "ticker" — both are accepted and behave identically; `value` can be a ticker, CIK, or company name either way. person/place coming soon.
valueYesTicker (e.g., "AAPL"), zero-padded CIK (e.g., "0000320193"), or company name (e.g., "Moderna") — names resolve via SEC EDGAR company-name match.

Schema Changelog

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

  1. Changed3 schema fields changed
    • changedInput schema / properties / type / description
      Previous value: -"Entity type. Only \"company\" supported today; person/place coming soon."New value: +"\"company\" or \"ticker\" — both are accepted and behave identically; `value` can be a ticker, CIK, or company name either way. person/place coming soon."
    • changedInput schema / properties / type / enum
      Previous value: -[
-  "company"
-]New value: +[
+  "company",
+  "ticker"
+]
    • changedInput schema / properties / value / description
      Previous value: -"Ticker (e.g., \"AAPL\") or zero-padded CIK (e.g., \"0000320193\"). Names not supported — use resolve_entity first if you only have a name."New value: +"Ticker (e.g., \"AAPL\"), zero-padded CIK (e.g., \"0000320193\"), or company name (e.g., \"Moderna\") — names resolve via SEC EDGAR company-name match."
  2. First observed

TDQS

A4.6/5.0
Behavior5/5

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

The description adds substantial behavior beyond the annotations (readOnlyHint, idempotentHint). It details the fan-out across SEC, XBRL, USPTO, USAspending, FDA Purple Book, DOL, GLEIF, and news, explains soft-fails (USPTO sunset), notes that empty sections are real 'no data', and clarifies that private companies return resolved:false with notes. This fully discloses behavior without contradicting any annotation.

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 essential information. It front-loads trigger phrases and the core purpose, then systematically lists return fields. Every sentence adds value (e.g., soft-fail notes, empty-section behavior). It is not concise, but it is efficient for the complexity it covers, so it earns a 4 rather than a 5.

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?

Given the tool is complex, has no output schema, and returns many heterogeneous fields, the description is exceptionally complete. It enumerates every return component (cik, recent_filings, fundamentals, patents, federal_contracts, fda_products, hiring, news, LEI) and explains edge cases like private companies and empty sections. An agent would know exactly what to expect and how to interpret results.

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 the baseline is 3. The description adds meaningful detail: it explains that type accepts 'company' or 'ticker' interchangeably and that value can be a ticker, zero-padded CIK, or company name, with name resolution via SEC EDGAR. It also notes the resolved_from/resolved_to behavior. This goes beyond the schema, so a 4 is warranted.

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 and resource: 'full cross-source profile of a US public company'. It distinguishes itself from sibling tools by explicitly saying 'ALWAYS PREFER over chaining single-pack SEC/XBRL/news lookups when the user asks for a holistic view', and it lists trigger phrases like 'Tell me about X'. This clearly separates it from tools like compare_entities or resolve_entity.

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 provides explicit usage triggers: 'Tell me about X', 'research Acme', 'brief me on Tesla', etc., and says to prefer this over chaining single-pack lookups. However, it does not explicitly state when to use an alternative (e.g., when the user asks for a specific filing or a single metric). Still, the context is strong and actionable, so it misses the top score only because no exclusions are given.

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