Skip to main content
Glama
xtdexw

Logo MCP

by xtdexw
OverviewSchemaRelated ServersScoreDiscussions
JavaScript
Remote

Logo MCP Server

一个基于 Model Context Protocol (MCP) 的 Logo 提取服务器，可以从任何网站提取高质量的 Logo 图标。

功能特性

  • 🎯 智能Logo识别 - 自动识别网站中的Logo元素

  • 🔍 多种提取策略 - 支持favicon、meta标签、图片分析等多种方式

  • 🎨 AI优化处理 - 可选的Logo优化和增强功能

  • 📐 多格式输出 - 支持PNG、SVG格式输出

  • 🔧 灵活配置 - 可自定义输出尺寸和格式

  • 🚀 MCP协议 - 完全兼容Model Context Protocol标准

Related MCP server: MCP Image Extractor

安装

作为MCP服务器使用

npx @pickstar-2025/logo-mcp

本地开发安装

git clone https://github.com/xtdexw/logo-mcp.git
cd logo-mcp
npm install
npm run build

使用方法

MCP工具调用

该服务器提供两个主要的MCP工具：

{
  "tool": "extract_logo",
  "arguments": {
    "url": "https://example.com",
    "optimize": true,
    "format": "both",
    "size": 256
  }
}

参数说明：

  • url (必需): 要提取Logo的网站URL

  • optimize (可选): 是否进行AI优化处理，默认为true

  • format (可选): 输出格式，可选值：png、svg、both，默认为both

  • size (可选): 输出图片尺寸（像素），默认为256

{
  "tool": "analyze_logo_candidates",
  "arguments": {
    "url": "https://example.com"
  }
}

参数说明：

  • url (必需): 要分析的网站URL

命令行使用

# 提取Logo
node cli.js extract https://example.com

# 分析Logo候选项
node cli.js analyze https://example.com

MCP配置

在你的MCP客户端配置文件中添加：

{
  "mcpServers": {
    "logo-mcp": {
      "command": "npx",
      "args": ["@pickstar-2025/logo-mcp"]
    }
  }
}

技术架构

  • TypeScript - 类型安全的开发体验

  • MCP Protocol - 标准化的模型上下文协议

  • Puppeteer - 网页内容抓取和分析

  • Sharp - 高性能图像处理

  • AI优化 - 智能Logo识别和优化

开发

构建项目

npm run build

运行测试

npm test

启动开发服务器

npm run dev

API参考

LogoExtractor类

主要的Logo提取类，提供以下方法：

  • extractLogo(url, options) - 提取指定网站的Logo

  • analyzeCandidates(url) - 分析网站的所有Logo候选项

LogoOptimizer类

Logo优化处理类：

  • optimize(logoData, options) - 优化Logo质量和格式

  • resize(logoData, size) - 调整Logo尺寸

许可证

MIT License - 详见 LICENSE 文件

贡献

欢迎提交Issue和Pull Request！

更新日志

v1.0.0

  • 初始版本发布

  • 支持基本的Logo提取功能

  • 实现MCP协议兼容

  • 添加AI优化功能

支持

如果你遇到任何问题，请：

  1. 查看 Issues

  2. 提交新的Issue描述问题

  3. 联系维护者

Made with ❤️ by CodeBuddy

Available Tools

2 tools
analyze_logo_candidatesC

分析网站的所有Logo候选项并返回详细信息

ParametersJSON Schema
NameRequiredDescriptionDefault
urlYes要分析的网站URL

TDQS

C2.9/5.0
Behavior2/5

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

With no annotations provided, the description carries the full burden of behavioral disclosure. It states the tool analyzes and returns details, but doesn't specify what kind of details, whether it's a read-only operation, potential side effects, rate limits, or authentication needs. This leaves significant gaps in understanding the tool's behavior.

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, efficient sentence that directly states the tool's purpose without any unnecessary words or fluff. It's appropriately sized and front-loaded, making it easy to parse quickly.

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

Completeness2/5

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

Given the lack of annotations and output schema, the description is incomplete for a tool that analyzes and returns details. It doesn't explain what '详细信息' (detailed information) includes, such as the structure or type of data returned, leaving the agent uncertain about the output format and content.

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?

The input schema has 100% description coverage, with the single parameter 'url' clearly documented in the schema. The description doesn't add any additional meaning or context beyond what the schema provides, such as URL format requirements or examples, so it meets the baseline for high schema coverage.

Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.

Purpose4/5

Does the description clearly state what the tool does and how it differs from similar tools?

The description clearly states the action ('分析' meaning 'analyze') and resource ('网站的所有Logo候选项' meaning 'all logo candidates of a website'), providing a specific purpose. However, it doesn't explicitly differentiate from the sibling tool 'extract_logo', which might have overlapping functionality, so it doesn't reach the highest score.

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

Usage Guidelines2/5

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

The description provides no guidance on when to use this tool versus the sibling 'extract_logo' or any alternatives. It lacks context about prerequisites, exclusions, or specific scenarios for application, leaving the agent with minimal usage direction.

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. Dates show when Glama detected each change.

  1. 2 tool updates
    • First observedanalyze_logo_candidates
    • First observedextract_logo
Install Server
RayrunBuild, deploy, and operate MCP servers without becoming an infrastructure team.ray.runAd

TDQS

B3.1/5.0
Disambiguation5/5

The two tools have clearly distinct purposes: one analyzes logo candidates on a website, while the other extracts a logo icon link from a URL. There is no overlap or ambiguity between them, making it easy for an agent to select the correct tool based on the task.

Naming Consistency5/5

Both tools follow a consistent verb_noun pattern (analyze_logo_candidates and extract_logo), using snake_case throughout. The naming is predictable and readable, with no deviations or mixed conventions.

Tool Count2/5

With only two tools, the server feels thin for a logo-related domain. While the tools cover analysis and extraction, there are likely gaps in functionality (e.g., no tools for creating, updating, or deleting logos), making the scope appear incomplete and potentially limiting for agents.

Completeness2/5

The tool surface is severely incomplete for a logo management domain. It lacks basic CRUD operations (e.g., create_logo, update_logo, delete_logo) and other expected functionalities like logo validation or comparison. Agents will encounter dead ends when trying to perform common logo-related tasks.

Resources

Unclaimed servers have limited discoverability.

Looking for Admin?

If you are the server author, to access and configure the admin panel.

Related MCP Connectors

Related MCP Servers

  • MCP-LOGO-GEN

    arekhalpern
    A
    license
    Not graded
    quality
    D
    maintenance
    MCP Tool Server for Logo Generation. This server provides logo generation capabilities using FAL AI, with tools for image generation, background removal, and image scaling.
    172
    GPL 3.0

  • mcp_search_images

    yanjunz
    A
    license
    Not graded
    quality
    D
    maintenance
    A search service based on multiple image APIs and icon generation capabilities, specifically designed for integration with Cursor MCP service. Supports image search, download, and AI-generated icons.
    16
    MIT

  • Scrapezy MCP Server

    kxkaloo
    -
    license
    B
    quality
    Not graded
    maintenance
    A Model Context Protocol server that enables AI models to extract structured data from websites through the extract\_structured\_data tool.
    1
    29
    -

Latest Blog Posts

MCP directory API

We provide all the information about MCP servers via our MCP API.

curl -X GET 'https://glama.ai/api/mcp/v1/servers/xtdexw/logo-mcp'

If you have feedback or need assistance with the MCP directory API, please join our Discord server