register_hash
Idempotent
Register 256-bit perceptual hash with LSH band indexing for strip-proof provenance. ($0.10 / 1 GCX)
Input Schema
TableJSON Schema
| Name | Required | Description | Default |
|---|---|---|---|
| image | Yes | Base64-encoded PNG/JPEG image |
studiomcphub
TDQS
A3.7/5.0
Behavior4/5
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
Annotations provide idempotentHint and non-destructiveHint; description adds cost and indexing method, with no contradictions.
Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.
Conciseness5/5
Is the description appropriately sized, front-loaded, and free of redundancy?
Single sentence with no fluff, includes cost and key technical details.
Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.
Completeness3/5
Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?
Lacks output description; agent cannot infer what the tool returns (e.g., hash ID or status), though annotations help.
Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.
Parameters3/5
Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?
Schema covers the only parameter fully; description adds context about hash type but no additional parameter-level details.
Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.
Purpose5/5
Does the description clearly state what the tool does and how it differs from similar tools?
The description clearly states it registers a 256-bit perceptual hash with LSH band indexing for strip-proof provenance, distinguishing it from siblings like verify_provenance.
Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.
Usage Guidelines2/5
Does the description explain when to use this tool, when not to, or what alternatives exist?
No explicit guidance on when to use this tool versus alternatives like verify_provenance or enrich_metadata; only the purpose is implied.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
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TDQS
A3.7/5.0
Disambiguation5/5
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.
Naming Consistency5/5
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.
Tool Count4/5
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.
Completeness5/5
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.