register_hash
Register 256-bit perceptual hash with LSH band indexing for strip-proof provenance. ($0.10 / 1 GCX)
Input Schema
| Name | Required | Description | Default |
|---|---|---|---|
| image | Yes | Base64-encoded PNG/JPEG image |
Register 256-bit perceptual hash with LSH band indexing for strip-proof provenance. ($0.10 / 1 GCX)
| Name | Required | Description | Default |
|---|---|---|---|
| image | Yes | Base64-encoded PNG/JPEG image |
Changes observed during successful MCP inspections. Dates show when Glama detected each change.
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
Annotations already establish the read/write safety profile (readOnly=false, destructive=false, idempotent=true). The description adds valuable behavioral context: the $0.10 cost, the specific LSH band indexing technique, and the 'strip-proof provenance' purpose, which go beyond what annotations convey.
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?
Two concise sentences that are front-loaded with the action and purpose. The cost note is included without redundancy. Every word contributes meaning, making it efficiently structured.
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 single-parameter schema and annotations that cover safety and side-effect hints, the description adequately explains the core function and cost. However, it does not mention expected return values or next steps, which would improve completeness given there is no output schema.
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 schema covers 100% of the single parameter 'image' with a clear description ('Base64-encoded PNG/JPEG image'). The tool description adds no further parameter-level details, so the baseline score of 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 the verb 'Register' and the specific resource '256-bit perceptual hash with LSH band indexing', making the tool's function unambiguous. It also distinguishes this tool from siblings like verify_provenance by focusing on the registration step.
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 strip-proof provenance registration but does not explicitly state when to use this tool over alternatives or mention exclusions. It provides context but lacks direct comparative guidance.
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.