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Search Artworks

search_artworks
Read-onlyIdempotent

Search the Minneapolis Institute of Art collection (~90k objects). Accepts free text ("monet water lilies") or Elasticsearch field syntax: artist:"Van Gogh", country:"China", department:"Asian Art", room:G3* — combinable with free text. Returns artist, date, medium, gallery location (on-view status), and image URL. Keyless.

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
limitNoMax results (default 10, max 25).
queryYesFree text or ES query-string field syntax. Examples: "monet", 'artist:"Van Gogh"', 'horse country:"China"', 'department:"European Art"'.
on_view_onlyNoIf true, only return artworks currently on view in a gallery.

Schema Changelog

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

  1. Changed1 schema field changed
    • addedInput schema / examples
      Added value: +[
      +  {
      +    "query": "monet water lilies"
      +  },
      +  {
      +    "limit": 5,
      +    "on_view_only": true,
      +    "query": "artist:\"Van Gogh\" country:\"Netherlands\""
      +  }
      +]
  2. First observed

TDQS

A4.2/5.0
Behavior4/5

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

Annotations already indicate readOnlyHint, openWorldHint, idempotentHint, destructiveHint. The description adds that the tool is 'Keyless' (no auth required) and lists return fields (artist, date, medium, gallery location, image URL), providing useful 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.

Conciseness5/5

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

Three sentences with no wasted words. Front-loaded with purpose, then syntax, then return fields. 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?

Given no output schema, the description adequately explains what the tool returns. It mentions collection size and keyless access. Minor omission: no mention of pagination, but limit parameter addresses this partially.

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% and all parameters have descriptions. The description reinforces parameter usage with examples (e.g., artist:"Van Gogh") and notes default limit of 10 and max 25, adding value beyond the schema.

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 states 'Search the Minneapolis Institute of Art collection (~90k objects)', which specifies the verb 'search' and the resource 'collection'. It distinguishes from siblings like get_artwork and search_within by focusing on free-text and ES field syntax across the full collection.

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

Usage Guidelines3/5

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

The description provides examples and explains acceptable syntax (free text or ES field syntax), implying usage for general collection search. However, it does not explicitly mention when to use this tool over siblings (e.g., search_within, get_artwork) or specify when not to use it.

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.