Get Hybrid_Premium 111-field NEST analysis (2K-6K tokens) + image. Deep AI visual analysis with color palette, composition, symbolism, emotional mapping. ($0.20 / 2 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?
Annotations already indicate read-only, idempotent, non-destructive behavior. The description adds cost ($0.20/2 GCX), output size (2K-6K tokens), and the inclusion of an image, which are not in annotations. No contradiction.
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 two sentences, front-loading the main deliverable and then detailing features and cost. No redundant information.
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 the tool's complexity and absence of an output schema, the description specifies the analysis fields, token range, and image inclusion. It does not detail the output format, but provides sufficient context for an agent to anticipate results.
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?
The single parameter is fully described in the schema with an example. The description adds no parameter-specific information, so baseline 3 applies.
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 this tool retrieves a structured analysis ('111-field NEST analysis') plus an image. The specific components (color palette, composition, symbolism, emotional mapping) distinguish it from siblings like extract_palette or get_artwork.
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 usage for deep AI visual analysis of artwork, with cost and token range providing context. It does not explicitly name alternatives or exclusions, but the 'Deep AI' phrasing signals it is for comprehensive analysis beyond basic retrieval.
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.