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 context about output tokens (2K-6K), cost ($0.20/2 GCX), and the AI nature of the analysis, enhancing transparency beyond the schema and 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?
The description is concise, comprising two to three sentences with no redundant information. It front-loads the key output and includes essential details about the analysis type and cost.
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 simple parameter set and no output schema, the description adequately describes the output. However, it lacks explicit usage context compared to siblings, slightly reducing completeness.
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 single parameter artifact_id. The description does not add new information about the parameter beyond what the schema provides, so a baseline score of 3 is appropriate.
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 a 'Hybrid_Premium 111-field NEST analysis' plus an image, specifying the type of deep AI visual analysis including color palette, composition, symbolism, and emotional mapping. This distinguishes it from siblings like get_artwork or extract_palette.
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 detailed analysis is needed but does not explicitly state when not to use or compare to alternatives. Cost is mentioned, suggesting careful use, but no direct guidance on selecting between siblings.
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.
Each tool targets a distinct operation—artwork retrieval, image processing, asset management, watermarking, etc.—with clear descriptions that prevent confusion. Even similar tools like enrich_metadata and get_artwork_oracle are differentiated by depth and purpose.
Tool names follow a consistent verb_noun pattern in snake_case (e.g., get_artwork, remove_background, register_hash). The few non-verb-starting names (compliance_manifest) are standard and do not break the overall pattern.
27 tools is slightly above the typical range but justifiable given the broad domain covering artwork access, image processing, and digital rights. Each tool serves a unique purpose without redundancy.
The tool set covers the full lifecycle: search, retrieve, analyze, edit, save, and verify assets. Gaps are minimal—e.g., no metadata deletion tool—but the core workflows are fully supported, and the inclusion of compliance and provenance tools adds value.