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Va Museum

by io.github.pipeworx-io

Search Objects

search_objects
Read-onlyIdempotent

Search the Victoria & Albert Museum (V&A) — the world's leading art & design museum. Find objects across 1M+ items (furniture, fashion, ceramics, photographs, paintings, jewellery, sculpture) by keyword. Returns id, title, maker, date, place, type, and thumbnail. Use the returned id with get_object for full details. Keyless.

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
pageNoPage number, 1-based (default 1).
limitNoMax results to return (default 15, max 100).
queryYesFree-text keyword search (e.g. "Jasper Morrison chair", "Tudor portrait", "Japanese ceramics").
with_imagesNoIf true, only return objects that have images (default false).

TDQS

A4.2/5.0
Behavior4/5

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

The annotations (readOnlyHint, idempotentHint, destructiveHint false) are consistent with the description. The description adds context about the scale (1M+ items) and return fields, but does not contradict any annotations. Disclosure is adequate.

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

Conciseness5/5

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

Three sentences: context, action, follow-up guidance. No filler. Front-loaded with the tool's purpose. Every sentence earns its place.

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 covers return fields (id, title, maker, etc.) and links to get_object. Missing explanation of pagination (page, limit) and with_images filter, but these are in the schema. Overall sufficient for a search tool.

Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.

Parameters3/5

Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?

Schema coverage is 100% with clear parameter descriptions. The description provides examples but does not add significant meaning beyond the schema. Per guidelines, baseline is 3.

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 searching the V&A museum collection by keyword, and explicitly distinguishes it from get_object by stating 'Use the returned id with get_object for full details.' This differentiates it from a 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 Guidelines4/5

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

The description tells when to use the tool (for initial keyword search) and when to use get_object (for full details). It also mentions it's keyless, implying no auth setup needed. However, it does not explicitly state when not to use it or compare to other sibling search tools like deep_research.

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

A3.7/5.0
Disambiguation3/5

Many tools have distinct purposes, but there is notable overlap between ask_pipeworx and ask_pipeworx_grounded (same underlying data query, different answer modes), and multiple polymarket tools (bet_research, polymarket_arbitrage, polymarket_edges) can confuse agents about which to use for a given betting query. Memory tools (remember/recall/forget) are clear, but the mix of museum, financial, and prediction market tools under one server increases ambiguity.

Naming Consistency3/5

All tool names use snake_case consistently, but the naming pattern is inconsistent: some start with a verb (search_objects, list_subscriptions, remember) while others start with a noun or modifier (ai_visibility_check, entity_profile, polymarket_arbitrage). The 'ask_' prefix is used twice, but overall there is no single predictable convention like verb_noun across the set.

Tool Count3/5

29 tools is high but not excessive for a general-purpose data server. However, the server is named 'Va Museum' which implies a narrow domain, making the count seem bloated. The set includes many tools unrelated to a museum (e.g., prediction markets, SEC filings), so the count is appropriate only if the server's actual scope is broad and multi-domain.

Completeness2/5

For a museum-focused server, the tool surface is severely incomplete, with only two museum-specific tools (search_objects, get_object) out of 29. Even as a general-purpose server, it lacks tools for common operations like updating or deleting resources, and the coverage of domains (e.g., no tool for creating or managing user data) feels ad hoc rather than systematically complete.

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