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Glama

Studio Ghibli

by io.github.pipeworx-io

Recent Changes

recent_changes
Read-onlyIdempotent

"What's new with X" / "latest on Y" / "what happened to Z this week / month / quarter" / "updates on Acme" / "news on Tesla recently" / "what's happening with Apple" — change feed for a company in the last N days/weeks/months in ONE parallel call. Fans out to SEC EDGAR (filings since since), GDELT→GNews fallback (news mentions in window — GDELT preferred, GNews when rate-limited or 5xx), USPTO (patents granted; PatentsView API sunset May 2025 so this soft-fails until reactivated). since accepts ISO date ("2026-04-01") or relative shorthand ("7d", "30d", "3m", "1y"). Returns structured changes[] grouped by source + total_changes count + pipeworx:// citation URIs. Use entity_profile instead when you want the static profile (filings + fundamentals + LEI + patents) regardless of window.

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
typeYesEntity type. Only "company" supported today.
sinceYesWindow start — ISO date ("2026-04-01") or relative ("7d", "30d", "3m", "1y"). Use "30d" or "1m" for typical monitoring.
valueYesTicker (e.g., "AAPL") or zero-padded CIK (e.g., "0000320193").

Schema Changelog

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

  1. First observed

TDQS

A4.8/5.0
Behavior5/5

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

Beyond the read-only/idempotent annotations, the description details the fan-out to SEC EDGAR, GDELT→GNews fallback, and USPTO with a soft-fail note, plus the return structure (changes[], total_changes, citation URIs). This adds substantial behavioral context beyond annotations.

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 somewhat long, but every sentence carries functional value—examples, source behavior, fallback logic, and alternative tool. The front-loaded examples effectively convey intent without being verbose.

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 the return format (changes[] grouped by source, total_changes, citation URIs) and covers source-specific behaviors and limitations. The alternative tool reference completes the context.

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%, but the description adds helpful guidance: `since` accepts ISO or relative shorthand with examples, and recommends '30d' or '1m' for typical monitoring. It also clarifies `value` as ticker or CIK, enriching the schema 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 clearly identifies the tool as a change feed for a company over a time window, with multiple natural-language examples. It explicitly contrasts with entity_profile (static profile) to distinguish from a key sibling tool.

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

Usage Guidelines5/5

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

The description gives concrete usage contexts ('What's new with X', 'latest on Y') and explicitly directs users to entity_profile for static profile needs, providing a clear alternative. It also specifies that the tool makes a single parallel call, implying efficiency.

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

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