Convert raster images to SVG vector format. Supports color and binary modes with precision controls. Returns raw SVG XML string. FREE. (FREE)
| Name | Required | Description | Default |
|---|---|---|---|
| mode | No | Vectorization mode | color |
| image | Yes | Base64-encoded PNG/JPEG image | |
| filter_speckle | No | Speckle filter (0-100, higher = fewer small artifacts) | |
| color_precision | No | Color clustering precision (1-10, higher = more colors) |
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
Annotations already declare read-only, idempotent, non-destructive. The description adds that it returns raw SVG XML and supports color/binary modes, providing useful behavioral context beyond 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 with two sentences that get to the point, though the repeated 'FREE' inline is redundant and unpolished.
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 tool's simplicity, the description covers the return format and main capability. No output schema exists, so explaining the raw SVG string return is important. The annotations cover safety aspects.
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 has 100% coverage with descriptions for all 4 parameters. The description mentions modes and precision controls but adds no specific parameter details beyond what the schema already provides.
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 uses the specific verb 'Convert' and resource 'raster images to SVG vector format', clearly defining the tool's function and distinguishing it from sibling image tools like resize_image or remove_background.
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 clearly implies that this tool is for vectorization but does not explicitly state when to use it over alternatives or mention any exclusions, so it lacks explicit usage 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.