get_artworkGet full details for one Minneapolis Institute of Art object by its numeric collection id — title, artist, date, medium, dimensions, credit line, department, gallery location, curatorial text, and image URL. Keyless.
| Name | Required | Description | Default |
|---|---|---|---|
| id | Yes | Mia object id, e.g. 1218 (Van Gogh, Olive Trees). |
Changes observed during successful MCP inspections. Dates show when Glama detected each change.
Input schema / examplesAdded value: +[
+ {
+ "id": 1218
+ }
+]Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
Annotations already declare readOnlyHint=true, destructiveHint=false, idempotentHint=true. The description adds 'Keyless' (no auth) and lists returned fields, which provides some extra behavioral context but no further details on rate limits, error handling, or output structure.
Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.
Is the description appropriately sized, front-loaded, and free of redundancy?
Two concise sentences, front-loaded with purpose. Every word contributes value: states action, resource, fields, and keyless nature. No verbosity.
Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.
Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?
For a simple get-by-id tool with one parameter, strong annotations, and no output schema, the description fully covers what the tool does, required input, and key behavioral hints (keyless). No gaps.
Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.
Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?
Schema coverage is 100% with a clear description and example for the 'id' parameter. The description reinforces that the id is numeric and relates to a collection object, but adds no new meaning beyond what the schema provides.
Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.
Does the description clearly state what the tool does and how it differs from similar tools?
The description clearly states the tool retrieves full details for one object by numeric collection id, listing specific fields (title, artist, etc.). It distinguishes from siblings like 'search_artworks' by focusing on a single, known object.
Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.
Does the description explain when to use this tool, when not to, or what alternatives exist?
The description implies use when a specific numeric id is known ('by its numeric collection id') but does not explicitly state when not to use or name alternatives. The 'Keyless' hint is helpful. Sibling awareness is not directly addressed.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
Add one secure layer between your agents and this server.
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.
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.
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.
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.