Get Human_Standard metadata (500-1200 tokens) + signed image URL for a museum artwork. ($0.10 / 1 GCX)
| Name | Required | Description | Default |
|---|---|---|---|
| artifact_id | Yes | Artifact ID from search results (e.g. 'met_437419') |
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
The annotations already declare readOnly, non-destructive, and idempotent behavior. The description adds valuable behavioral context—output size (500-1200 tokens), the signed image URL aspect, and the cost ($0.10 / 1 GCX)—which goes beyond what annotations provide. It does not detail URL expiry or auth, but given strong annotations, this is acceptable.
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?
The description is a single, well-structured sentence that packs in the resource, outputs, token count, and cost without redundancy. Every phrase earns its place.
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 read-only tool with one parameter and existing annotations, the description and schema are sufficient to invoke the tool correctly. It names the return values at a high level and notes pricing. It could have explained Human_Standard or URL expiry, but these are not essential for using the tool.
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 ('Artifact ID from search results (e.g. 'met_437419')'). The tool description adds no additional parameter meaning beyond the schema, but the baseline of 3 applies since the schema already does the heavy lifting.
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 uses a specific verb ('Get'), names the resource ('museum artwork'), and states the exact outputs ('Human_Standard metadata (500-1200 tokens) + signed image URL'). This clearly distinguishes it from sibling tools by anchoring to museum artwork and the Human_Standard format.
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?
No explicit when-to-use or when-not-to-use guidance is provided. The description only states what the tool does, without mentioning alternatives like get_artwork_oracle or get_asset, or any prerequisites/conditions for use.
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.
Most tools have distinct purposes, but there is some overlap between get_artwork and get_artwork_oracle, which both retrieve artwork metadata with different detail levels, potentially causing confusion. Other tools like enrich_metadata and infuse_metadata also have related but distinct functions, but descriptions help clarify differences.
Tool names generally follow a consistent verb_noun pattern (e.g., check_balance, delete_asset, resize_image), with minor deviations like mockup_image (noun_verb) and get_artwork_oracle (longer compound name). Overall, the naming is readable and predictable, though not perfectly uniform.
With 27 tools, the count is borderline high for a single server, as it covers a broad range of functionalities from artwork retrieval to image processing and compliance. While each tool seems useful, the scope feels heavy and could overwhelm agents, suggesting it might be better split into more focused servers.
The tool set provides comprehensive coverage for digital asset management, artwork analysis, and image processing, including CRUD operations (save_asset, get_asset, list_assets, delete_asset), metadata enrichment, compliance, and various image utilities. No obvious gaps are present for the stated domain.