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Quiver-MCP

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quiver-mcp

npm version npm downloads License: MIT

QuiverAI 的 MCP 服务器 — 直接从 Claude（或任何兼容 MCP 的客户端）使用 AI 从文本提示生成 SVG 并将栅格图像矢量化。

示例

由 Claude 调用此 MCP 生成。在 n: 3, temperature: 0.9 的设置下，每个生成耗时约 60 秒。两个提示词都记录在工具描述中，因此 Claude 知道生成配方。

提示词： exploded isometric view of a Montblanc Meisterstück fountain pen, technical blueprint drawing, thin line art, dotted grid background, labeled components, engineering illustration

提示词： Japanese crane in traditional woodblock illustration style with warm earth tones
说明： Use a warm muted palette with detailed feather work

更多变体请查看 examples/。

Related MCP server: nakkas

要求

安装

Claude Desktop

添加到你的 claude_desktop_config.json：

{
  "mcpServers": {
    "quiverai": {
      "command": "npx",
      "args": ["-y", "@syntropic/quiver-mcp"],
      "env": {
        "QUIVERAI_API_KEY": "your_api_key_here"
      }
    }
  }
}

手动

npm install -g @syntropic/quiver-mcp
QUIVERAI_API_KEY=your_api_key_here quiver-mcp

工具

generate_svg

从文本提示生成一个或多个 SVG。

参数

类型

必需

描述

prompt

string

是

要生成的 SVG 的文本描述

model

string

是

模型 ID（使用 list_models 查看选项）

instructions

string

否

额外的样式或格式指导

n

number

否

要生成的 SVG 数量（默认：1）

temperature

number

否

采样温度 0–2（默认：1）

references

array

否

最多 4 个图像参考（{url} 或 {base64}），用于调色板和构图指导。样式关键词仍需包含在提示文本中。

outputPath

string

否

保存 SVG 到磁盘的绝对文件路径。对于多个变体（n > 1），文件将以 _1, _2 等后缀保存。父目录会自动创建。

提示词技巧

工具描述中包含了详尽的提示词指南，简而言之：

  • 将提示词结构化为三个部分：主体（具体对象）、样式（如 line art、isometric、flat monochrome 等美学关键词）和 调色板（尽可能使用十六进制代码）。

  • 使用模型已知的著名物理对象。避免抽象的软件概念（如 AI agent、workflow）——请改用物理隐喻。

  • 为了探索效果，请在 temperature: 0.9 下生成 3 个以上的变体。有些生成结果会出现损坏的尾部；额外的变体可以为你提供更多选择。

vectorize_svg

将栅格图像（PNG、JPG 等）转换为 SVG。

参数

类型

必需

描述

model

string

是

模型 ID

image

object

是

要矢量化的图像 — {url} 或 {base64}

autoCrop

boolean

否

在矢量化前裁剪到主要主体（默认：false）

targetSize

number

否

矢量化前调整的正方形目标像素大小

temperature

number

否

采样温度 0–2（默认：1）

outputPath

string

否

保存 SVG 到磁盘的绝对文件路径。父目录会自动创建。

list_models

列出所有可用的 QuiverAI 模型及其支持的操作和定价。

环境变量

变量

描述

QUIVERAI_API_KEY

必需。 你的 QuiverAI API 密钥

开发

npm install
npm run build   # compile TypeScript
npm run dev     # watch mode

许可

MIT

Available Tools

3 tools
generate_svgA

Generate one or more SVGs from a text prompt using QuiverAI. Returns raw SVG markup.

Prompt guide

A good prompt has three parts: subject (specific object), style (aesthetic keywords), and color palette (hex codes if possible).

What works

  • Use concrete, famous physical objects the model has seen (AirPods, Nike Dunks, Shure SM7B, Montblanc pen, Leica camera, Nest thermostat, espresso machines). Cylindrical/round objects explode especially cleanly in isometric style.

  • Name the style explicitly: 'line art', 'hand drawn', 'duotone', 'flat monochrome icon', 'geometric', 'minimalist', 'isometric', 'blueprint'.

  • Specify colors with hex codes: 'background: #e9edc9 and logo in #fb8500'.

  • Add composition framing: 'centered icon', 'wide horizontal logo'.

  • Prompt modifiers: 'geometric' → angular shapes, 'detailed' → more elements, 'simple' → clearer shapes, 'minimalist' → fewer details, 'flat monochrome' → single-color, 'duotone' → two-color.

What does NOT work

  • NEVER mention 'AI', 'machine learning', 'voice assistant', 'workflow automation', or abstract software concepts — produces garbage. Use physical metaphors instead (microphone for voice, watch movement for precision).

  • Abstract concepts without physical objects: 'knowledge graph', 'automation pipeline', 'data flow'.

  • Obscure B2B hardware the model hasn't seen (e.g. Loxone Miniserver → generic blob).

  • 'minimalist line icon' constraints — model ignores them and fills with color.

Iteration strategy

Start specific, not vague. Bad: 'Tech logo'. Better: 'Tech startup logo with geometric shapes, blue gradient'. Best: 'SaaS productivity logo with connected geometric nodes, electric blue to purple gradient, clean modern style'.

Verified template

exploded isometric view of a {FAMOUS_OBJECT}, technical blueprint drawing, thin line art, dotted grid background, labeled components, engineering illustration

Known issues

  • ~1 in 10 generations have corrupted SVG tails (malformed XML). Generate 3+ variants as insurance.

  • Model may ignore 'no fills'/'monochrome' and hardcode its own palette. Post-process with find/replace for brand colors.

  • First call may 504 — retry succeeds.

ParametersJSON Schema
NameRequiredDescriptionDefault
promptYesWHAT to generate. Be specific: name a concrete famous object, add style keywords, and specify colors with hex codes. Example: 'Heraldic lion crest with ornate medieval style details and gold gradient accents'. Never use abstract concepts like 'AI agent' or 'workflow' — use physical metaphors instead.
modelYesModel ID to use. Recommended: 'arrow-preview' (Arrow 1.0, #1 on SVG Arena). Use list_models to discover all options.
instructionsNoHOW it should look — style guidance separate from the subject. Think of prompt as 'what' and instructions as 'how'. Example: prompt='Japanese crane', instructions='Use a warm muted palette with detailed feather work'.
nNoNumber of SVG variants to generate (max 16). Recommended: 3+ at higher temperature for best results, since ~1 in 10 generations can have corrupted tails.
temperatureNoSampling temperature (0–2). Lower (0.4) = more consistent, higher (0.9) = more creative variation. Use 0.9 with n≥3 for exploration.
referencesNoUp to 4 reference images for style, color, and composition guidance. References pull palette/color hints from the image, but style keywords ('blueprint', 'isometric', 'flat') must still be in the text prompt — references alone won't change drawing style.
outputPathNoOptional absolute file path to save the SVG(s) to disk. If omitted, SVG markup is returned in the response only. For multiple variants (n > 1), files are saved with _1, _2 … suffixes. Parent directories are created automatically.

TDQS

A4.9/5.0
Behavior5/5

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

With no annotations, the description fully covers behavioral traits: it mentions return format (raw SVG markup), corruption rate (~1 in 10), timeout behavior (504 with retry), and model's tendency to ignore palette constraints. This gives the agent a clear understanding of tool behavior.

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 long but well-structured with clear headings and sections. It front-loads the core purpose and then provides detailed guidance. While verbose, every section serves a purpose; however, some redundancy could be trimmed for brevity.

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 (7 parameters, no output schema, no annotations), the description is remarkably complete. It covers prompt crafting, iteration strategies, known issues, and error recovery. The 'Prompt guide' alone provides rich context that an agent needs to succeed.

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

Parameters5/5

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

Although schema description coverage is 100%, the description adds substantial value beyond schema. The 'Prompt guide' provides concrete examples, do's and don'ts, and detailed reasoning for parameters like n and temperature. It also explains how references work and their limitations, which the schema does not fully convey.

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 'Generate one or more SVGs from a text prompt using QuiverAI. Returns raw SVG markup.' It specifies the verb (generate), resource (SVGs), and distinguishes from sibling tools like list_models and vectorize_svg.

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 provides extensive guidelines on when and how to use, including prompt structure, what works, what does not work, iteration strategy, and a verified template. It also covers known issues like corrupted tails and 504 errors, giving failure recovery guidance.

Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.

list_modelsA

List all models available on QuiverAI, including supported operations (svg_generate, svg_vectorize, etc.) and pricing.

ParametersJSON Schema
NameRequiredDescriptionDefault

No parameters

TDQS

A4.2/5.0
Behavior3/5

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

No annotations, but description is straightforward (list only). Does not mention read-only nature or auth requirements, but these are implicit for a list operation.

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?

Single sentence, no wasted words, front-loaded with purpose.

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

Completeness4/5

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

Complete for a simple list tool with no parameters and no output schema. Describes what is included (operations, pricing). Could mention output format but not critical.

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?

No parameters in schema; baseline for 0 params is 4. Description adds no param info (none needed).

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?

Clearly states verb (List), resource (models), and includes what info is returned (supported operations and pricing). Distinguishes from siblings generate_svg and vectorize_svg which are different actions.

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?

Implied usage: use to discover available models before generating or vectorizing. No explicit when-not-to-use or alternatives, but context makes it clear.

Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.

vectorize_svgA

Convert a raster image (PNG, JPG, etc.) into an SVG using QuiverAI. Provide the image as a URL or base64-encoded string.

ParametersJSON Schema
NameRequiredDescriptionDefault
modelYesModel ID to use. Use list_models to find models that support svg_vectorize.
imageYesThe image to vectorize — either a URL or base64 data.
autoCropNoAuto-crop to the dominant subject before vectorizing. Defaults to false.
targetSizeNoSquare resize target in pixels before vectorizing.
temperatureNoSampling temperature (0–2). Defaults to 1.
outputPathNoOptional absolute file path to save the vectorized SVG to disk. If omitted, SVG markup is returned in the response only. Parent directories are created automatically.

TDQS

A3.5/5.0
Behavior2/5

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

No annotations, so description carries full burden; it lacks details on failure modes, rate limits, output quality, or side effects beyond basic conversion.

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?

Two sentences, front-loaded with purpose, no extraneous information—efficient and clear.

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

Completeness3/5

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

Covers basic purpose and input method but omits return value format, side effects of optional parameters, and behavioral traits; adequate given schema coverage but incomplete.

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 coverage is 100% with detailed parameter descriptions, so description adds no new semantics beyond mentioning input image formats; baseline score applies.

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?

Description clearly states the tool converts raster images (PNG, JPG) to SVG using QuiverAI, distinguishing it from sibling generate_svg which likely creates SVGs from scratch.

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

Usage Guidelines3/5

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

Implies usage when a raster image needs conversion, but no explicit guidance on when not to use or alternatives like generate_svg or list_models.

Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.

Tool Schema Changelog

Recent tool additions, removals, and schema changes observed during successful MCP inspections.

  1. 3 tool updatesv0.1.0
    • First observedgenerate_svg
    • First observedlist_models
    • First observedvectorize_svg
Deploy Server
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TDQS

A4/5.0

Scored across 3 tools

Disambiguation5/5

Each tool has a distinct purpose: generating SVGs from prompts, listing models, and vectorizing images. There is no overlap.

Naming Consistency5/5

All tool names follow consistent snake_case with verb_noun pattern (generate_svg, list_models, vectorize_svg).

Tool Count4/5

Three tools is on the low side but reasonable for a focused server. The scope is narrow enough that each tool earns its place.

Completeness3/5

Core SVG creation (from text and images) and model listing are covered, but missing operations like fetching/updating/deleting individual SVGs or batch processing.

Maintenance

ActivityInactive
ResponsivenessNo issues

Resources

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