webbersites-x402-data-api

post_vectorize

High-quality image vectorization powered by Vectorizer.AI: POST a public image URL or base64 (PNG/JPEG/GIF/BMP/WebP, up to 10 MB) and get a production-grade vector back — SVG by default, or PNG/PDF/EPS/DXF. Full-color tracing, clean paths, ready for print, cutting, and scaling. The premium finish for logos, icons, sketches, and raster art. ($0.02 per call, paid via x402)

Input Schema

TableJSON Schema

Name	Required	Description
`url`	No	Public URL of the raster image to vectorize (PNG, JPEG, GIF, BMP, WebP; max 10 MB)
`mode`	No	production (default, full quality), preview, or test
`scale`	No	Output size multiplier vs input, 0-100 (e.g. 2 = double size)
`options`	No	Raw passthrough for any documented Vectorizer.AI option, e.g. {"output.gap_filler.enabled": false} — see vectorizer.ai/api
`palette`	No	Force a specific palette: array of hex or CSS color names — output only uses these colors
`group_by`	No	SVG shape grouping: none (default), color, parent, or layer
`draw_style`	No	fill_shapes (default), stroke_shapes, or stroke_edges (line-art outlines)
`max_colors`	No	Limit the color count, 0-256 (0 = unlimited). Great for flat/logo looks
`min_area_px`	No	Drop shapes smaller than this many pixels (0-10000) — despeckling
`image_base64`	No	Base64-encoded image as an alternative to url (data URIs accepted)
`output_format`	No	svg (default), png, pdf, eps, or dxf

Output Schema

TableJSON Schema

Name	Required	Description	Default
`svg`	No
`content_type`	No
`image_base64`	No
`output_bytes`	No
`output_format`	No

Schema Changelog

Changes observed during successful MCP inspections. Dates show when Glama detected each change.

First observedJul 9, 2026

TDQS

A4.5/5.0

Behavior5/5

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

The description discloses behavioral traits beyond annotations: cost ($0.02 per call), supported input formats, output formats, size limits (10 MB), and quality attributes (full-color tracing, clean paths). It does not contradict annotations; readOnlyHint=false is consistent with a mutation (POST) tool.

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?

The description is a single, well-structured paragraph that front-loads the main action and key details. Every sentence adds value: purpose, input, output, quality, pricing. No redundant or wasted words.

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 the tool's complexity (11 parameters, 0 required, 100% schema coverage, output schema present), the description provides sufficient context: use cases, quality claims, pricing, and output format options. It covers everything an agent needs to decide when and how to use the tool.

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 description coverage is 100%, so baseline is 3. The description adds minimal extra meaning beyond what the schema provides for individual parameters; it mentions default output format (SVG) and general quality but does not elaborate on parameter usage or constraints beyond schema.

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 the tool's purpose: 'High-quality image vectorization' with a specific verb ('POST') and resource (public image URL or base64). It distinguishes itself from siblings by mentioning production-grade output and premium finish for logos, icons, etc., which is unique among the listed tools.

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

Usage Guidelines4/5

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

The description provides clear context on when to use: 'premium finish for logos, icons, sketches, and raster art.' However, it does not explicitly exclude alternatives or mention when not to use this tool versus other similar tools, so it lacks explicit when-not guidance.

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, but the SEO-related tools (head_check, full_audit, site_audit, etc.) overlap in scope, potentially causing confusion despite clear descriptions.

Naming Consistency5/5

Tool names consistently follow a get_/post_/delete_ verb pattern with descriptive noun phrases (e.g., get_seo_head_check, post_store_collection), with no mixing of naming conventions.

Tool Count2/5

With 46 tools covering a wide breadth of domains (SEO, accessibility, music, crypto, linting, etc.), the count is excessive for a single server, feeling unfocused and heavy.

Completeness4/5

The tool set covers most core operations for each sub-domain, but minor gaps exist (e.g., missing update for datastore, limited music operations).

post_vectorize

Input Schema

Output Schema

Schema Changelog

TDQS

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TDQS

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