get_artworkGet the rich detail for a single Met collection object by its objectID — artist bio, medium, dimensions, department, classification, culture, period, credit line, image URLs, tags, and gallery number. Keyless.
| Name | Required | Description | Default |
|---|---|---|---|
| object_id | Yes | The Met objectID, e.g. 436535. |
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
Annotations already declare readOnly, openWorld, idempotent, non-destructive. Description adds value with 'Keyless' disclosure and specifics of returned fields (artist bio, medium, etc.), enhancing transparency 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.
Is the description appropriately sized, front-loaded, and free of redundancy?
Single sentence of ~30 words, front-loaded with purpose and key details. No redundancy, efficient communication.
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?
Given rich annotations, full schema coverage, and simple input, the description sufficiently covers what the tool returns and usage note. No missing critical information.
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 provides 100% coverage for the single parameter with description and example. Description reinforces by mentioning 'by its objectID', but adds no new semantic detail beyond schema.
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?
Description clearly states it retrieves rich detail for a single Met object by objectID, listing specific fields. Distinguishes from sibling 'search_artworks' by emphasizing single object retrieval.
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?
Mentions 'Keyless' (no API key needed) but lacks explicit when-to-use or when-not-to-use guidance compared to siblings like 'search_artworks'. Usage context is implied but not stated.
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.
The server mixes two Met-specific tools (get_artwork, search_artworks) with a large set of generic Pipeworx tools (ask_pipeworx, bet_research, etc.), making it unclear which tools actually relate to the Met museum. Agents will struggle to distinguish the domain-specific tools from the general data tools.
Tool names follow no consistent pattern: Met-specific tools use get_/search_/list_ prefixes, while Pipeworx tools use diverse patterns (ask_, bet_, compare_, discover_) and some use underscores while others lack verbs. The inconsistency increases cognitive load.
At 29 tools, the count is high but not unreasonable for a combined server. However, only 3 tools are Met-specific, so the count feels inflated by unrelated tools. A more focused Met server would have fewer tools.
For a Met museum server, the tool surface is severely limited: only search, get by ID, and list departments. Missing operations like filtering by artist, retrieving related objects, or accessing collection highlights. The domain coverage is incomplete.