Place your design onto product mockups (t-shirt, poster, canvas, phone case, mug, tote bag). Instant product visualization for e-commerce and print-on-demand. FREE. ($0.10 / 1 GCX)
| Name | Required | Description | Default |
|---|---|---|---|
| image | Yes | Base64-encoded design image | |
| product | No | Product type | tshirt |
| background_color | No | Background hex color | #f5f5f5 |
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
The annotations already declare the tool as read-only, idempotent, and non-destructive. The description adds context about cost (FREE with a GCX fee) and instant speed, but does not disclose details about output format or file handling, which is a minor gap given no output schema.
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 compact, front-loads the primary purpose, and organizes additional context (use case, pricing) in short sentences. The pricing note 'FREE' is extra but not detrimental.
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 tool with three parameters, full schema coverage, and helpful annotations, the description adequately explains the core function and use case. However, it omits details about the output format or any image requirements, which would be useful given the absence of an 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 input schema fully documents all three parameters, including descriptions, types, enums, and defaults. The description repeats the product list but adds no new semantic information about how to use the parameters, so it meets the baseline for schema coverage.
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's purpose with a specific verb ('Place') and a defined resource ('your design onto product mockups'), and enumerates the supported product types. This makes it distinct from sibling tools, none of which offer mockup generation.
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?
It provides a clear use case for e-commerce and print-on-demand, which signals when to employ this tool. However, it does not explicitly name alternatives or exclusions, though no sibling tool offers mockup functionality.
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.