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upload_image

Upload a JPEG or PNG image and get back a hosted URL you can use with submit_design.

This tool is useful when your agent framework produces images as artifacts (e.g. base64 strings) and you need to upload them before submitting a design.

Provide the image as ONE of: image_base64, base64-encoded JPEG/PNG, with or without data URI prefix. image_url, publicly accessible image URL (max 5 MB). image_chunks, array of base64 strings that will be concatenated server-side. Use this if your base64 string is too large for a single parameter.

Returns: { image_id, image_url, format, size_bytes } Pass the returned image_url to submit_design's image_url parameter.

ALTERNATIVE: If your runtime truncates large base64 strings (common with LLM output token limits), you can submit designs by email instead:

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
image_urlNoPublicly accessible JPEG/PNG URL (max 5 MB)
image_base64NoBase64-encoded JPEG/PNG, with or without data URI prefix
image_chunksNoArray of base64 strings, concatenated server-side to form the full image. Use when base64 is too large for a single field.

TDQS

A4.6/5.0
Behavior4/5

Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?

With no annotations provided, the description carries the full burden. It discloses the three input methods, chunk concatenation behavior, size limits, and return structure. It also notes the possibility of truncation and suggests an email workaround. However, it doesn't mention error handling, validation, or side effects beyond hosting, so a 4 is warranted.

Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.

Conciseness4/5

Is the description appropriately sized, front-loaded, and free of redundancy?

The description is longer than average due to the alternative email path, but it is well-structured with clear sections and line breaks. Every sentence provides operational value, and the return format is explicitly shown. It could be trimmed slightly, but the organization and formatting earn it a 4.

Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.

Completeness5/5

Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?

Given that there is no output schema, the description fully explains the return value ({ image_id, image_url, format, size_bytes }) and how to use it with submit_design. It also provides a fallback method for edge cases, making the tool description self-contained. This is rare and highly complete.

Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.

Parameters4/5

Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?

Schema coverage is 100%, so baseline is 3. The description adds value by explaining each parameter beyond the schema: data URI prefix allowance, max 5 MB for image_url, and when to use image_chunks ('if your base64 string is too large'). This extra guidance helps the agent select the correct input method.

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 opens with a specific verb and resource: 'Upload a JPEG or PNG image and get back a hosted URL you can use with submit_design.' This clearly states the tool's function and its relationship to a sibling tool, distinguishing it from other tools. The purpose is unambiguous and action-oriented.

Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.

Usage Guidelines5/5

Does the description explain when to use this tool, when not to, or what alternatives exist?

The description explicitly states when to use this tool ('when your agent framework produces images as artifacts') and provides a direct alternative (email submission) with specific conditions ('If your runtime truncates large base64 strings'). It also instructs how to use the result with submit_design, offering clear usage context and exclusions.

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

A3.9/5.0
Disambiguation4/5

Most tools have distinct purposes with clear category tags. Minor potential confusion between get_current_brief vs list_briefs and get_brand vs get_brand_mcp_endpoint, but descriptions clarify intent.

Naming Consistency5/5

All tools follow a consistent verb_noun pattern using lowercase with underscores, e.g., check_agent_standing, list_drops, submit_design. No mixing of conventions.

Tool Count3/5

32 tools is on the high side, but the server covers a wide domain including browsing, purchasing, design submission, concierge, and marketing. Each tool serves a specific function, though some consolidation could be possible.

Completeness4/5

The tool surface covers the full lifecycle of browsing, purchasing, design creation, commissions, and concierge services. Minor gaps like refund handling are absent, but the core workflows are well-supported.