A
licenseB
qualityD
maintenanceEnables decoding QR codes from images provided as either base64 data URLs or HTTP(S) image URLs. Supports common image formats and returns the decoded text content with clear error handling.
2
16
MIT
QR Reader MCP Server
by Endymionus
This server enables AI agents to decode QR codes and other barcodes from images, with image quality diagnostics and enhancement capabilities.
Decoding: Scan entire images for barcodes (QR, DataMatrix, PDF417, 1D codes like EAN/UPC, etc.) with
decode_qrcode_full, optionally filtering by symbology.Quality Diagnostics: Returns blur, contrast, glare, noise, and ISO modulation metrics, plus a
result_code(SUCCESS, RETRYABLE, etc.) to guide next steps.Automatic Enhancement:
auto_enhancetries up to 7 enhancement strategies (upscale, sharpen, contrast, denoise, combos) until a decode succeeds.Manual Enhancement:
enhance_and_decodeapplies custom operations to a user-defined bounding box.Input Flexibility: Accepts local file paths, Base64 strings, or public URLs.
Safety & Configuration: SSRF protection, size limits, read-only mode (disables enhancement tools), and configurable environment variables for logging and processing thresholds.
Click on "Install Server".
Wait a few minutes for the server to deploy. Once ready, it will show a "Started" state.
In the chat, type
@followed by the MCP server name and your instructions, e.g., "@QR Reader MCP ServerRead the QR code in this image and tell me what it says"
That's it! The server will respond to your query, and you can continue using it as needed.
Here is a step-by-step guide with screenshots.
QR Reader MCP Server
一个 MCP 工具，让 AI 真正读懂二维码。视觉模型能"看到"有码但解不了——这个工具补上了这一步。不只返回内容，还告诉 Agent 码清不清楚、值不值得增强重试。
decode_qrcode_full 只需图片输入即可工作，任何对接了 MCP 的模型都能直接调用。auto_enhance 在质量不佳时自动尝试增强恢复，无需手动干预。enhance_and_decode 适合需要精确控制的场景。
🤖 通过 AI Agent 安装 — 把这条链接发给你的 AI Agent，它会自动完成安装配置：
https://raw.githubusercontent.com/Endymionus/qr-reader-mcp-server/main/INSTALL_FOR_AGENT.md
解决什么问题
AI 模型面临一个尴尬的问题：有视觉能力的模型能认出"图里有一个二维码"，但二维码解码走的是像素→二进制数据→文本的算法路径，模型做不到；纯文本模型更不用说，连"看到图里有码"都做不到。
QR Reader MCP Server 把 zbar 二维码解码能力嵌入 AI 工作流——只要有图片输入，就能读出码里的内容。视觉模型可主动触发，纯文本模型通过用户引导触发。
工作流
图片 → decode_qrcode_full → SUCCESS? → 直接用
↓
RETRYABLE? → auto_enhance → 自动恢复
↓
需精确控制? → enhance_and_decode三个工具各司其职——decode_qrcode_full 先行诊断，auto_enhance 一键自动恢复，enhance_and_decode 精确手动控制。
三个工具
工具 | 说明 |
| 一键自动恢复 — 7 种增强策略有序尝试，首次成功即返回。成功后除 JSON 诊断外还返回增强区域的 PNG 截图（ImageContent），供多模态模型直接查看增强效果 |
| 手动精控 — 对指定区域执行自定义增强后再解码 |
| 扫描整张图片，返回所有条形码的内容 + 质量诊断。支持 |
比成功/失败更多的信息
实际场景中二维码质量参差不齐——模糊、反光、太小、对比度不够。MCP 在返回解码结果的同时，也附带了图像质量数据（模糊度、对比度[标准差+ISO15415调制比]、反光比例[空间方差]）和 result_code。Agent 拿到这些信息后，可以自然地告诉用户"这个码有点模糊，换个角度拍"或者"反光挡住了，调整一下光源"，也可以直接调用 auto_enhance 自动修复。
Related MCP server: QR Code Generator MCP
快速开始
前置依赖
# Ubuntu / Debian
sudo apt install libzbar0
# macOS
brew install zbar
# Windows (choco — CI verified)
choco install zbar安装运行
# 推荐：uvx 一行安装（Python 3.10+, 自动处理依赖）
uvx qr-reader-mcp-server
# 或：git clone + pip 安装
pip install . # 基础版（~15 MB）
pip install ".[full]" # 全功能版（~120 MB）两种安装方式 — 按需选择：
# ── 基础版（推荐，~15 MB）───────────────────────────────
# pyzbar 解码 + 全部 3 个工具 + 质量诊断
pip install .
# ── 全功能版（需要最强能力）─────────────────────────────
# 基础版全部功能 + OpenCV 解码回退 + finder-pattern 畸变检测
pip install ".[full]"基础版 | 全功能版 | |
下载大小 | ~15 MB | ~120 MB |
decode_qrcode_full | ✅ | ✅ |
auto_enhance | ✅ | ✅ |
enhance_and_decode | ✅ | ✅ |
质量指标（blur/contrast/glare/noise/modulation） | ✅ | ✅ |
distortion (finder-pattern 几何畸变) | ❌ | ✅ |
OpenCV 解码回退 | ❌ | ✅ |
# uvx 安装 → 启动（stdio 模式）
uvx qr-reader-mcp-server
# 或 pip 安装后启动
python -m qr_reader.serverMCP 客户端配置
Claude Desktop / VS Code Copilot / Cursor：
按你的安装方式选一个。用 uvx 安装（推荐）：
{
"mcpServers": {
"qr-reader": {
"command": "uvx",
"args": ["qr-reader-mcp-server"],
"env": {
"LOG_LEVEL": "info",
"READ_ONLY_MODE": "false"
}
}
}
}用 pip 安装：
{
"mcpServers": {
"qr-reader": {
"command": "python",
"args": ["-m", "qr_reader.server"],
"env": {
"LOG_LEVEL": "info",
"READ_ONLY_MODE": "false"
}
}
}
}⚠️ 两种配置不能混用：uvx 隔离环境下
python -m qr_reader.server找不到包；pip 环境下uvx会重新拉取而不是用本地安装。
Docker：
{
"mcpServers": {
"qr-reader": {
"command": "docker",
"args": [
"run", "-i", "--rm",
"ghcr.io/Endymionus/qr-reader-mcp-server"
]
}
}
}环境变量
变量 | 默认值 | 说明 |
|
| 日志级别： |
|
| 设为 |
|
| 图片大小上限（字节，默认 10 MB） |
|
| 图片长边超过此值自动缩放 |
|
| 增强管线单步输出长边上限（upscale 超限即拒绝，防链式放大 OOM） |
|
| Laplacian 方差归一化阈值（模糊贡献 = 阈值 ÷ 实际方差） |
|
| 对比度归一化锚点（实际对比度越接近此值越健康） |
|
| ISO 15415 调制比归一化锚点 |
|
| 反光归一化上界（超过视为严重反光） |
|
| 噪声归一化上界 |
|
| 畸变角度归一化上界（度） |
|
| 畸变腿比归一化下界 |
工具说明
decode_qrcode_full
对整张图片进行二维码识别和解码，返回结构化结果和质量指标。
输入参数：
参数 | 类型 | 必填 | 说明 |
| string | 三选一 | 本地图片绝对路径（推荐——无管道开销） |
| string | 三选一 | Base64 编码的图片 |
| string | 三选一 | 图片 URL |
返回示例：
blur_score是拉普拉斯方差：数值越大图像越清晰（高对比度边缘多），越小越模糊。contrast是归一化标准差，越大对比度越高。两者方向相反，Agent 集成时请注意。
{
"success": true,
"result_code": "SUCCESS",
"results": [
{
"content": "https://example.com",
"bbox": [50, 60, 200, 200],
"type": "QRCODE",
"raw_bytes": "..."
}
],
"analysis": {
"total_detected": 1,
"modulation": 0.92,
"quality": {
"blur_score": 128.5,
"contrast": 0.72,
"glare_ratio": 0.05,
"noise_level": 12.3
}
},
"suggestion": null,
"image_size": [600, 800],
"resize_factor": 1.0
}auto_enhance
质量不佳时的一键自动恢复。7 种增强策略有序尝试（upscale / sharpen / contrast / denoise / 组合策略），首次解码成功即返回，无需手动指定 bbox 或 operation。
输入参数：
参数 | 类型 | 必填 | 说明 |
| string | 三选一 | 本地图片绝对路径 |
| string | 三选一 | Base64 编码的图片 |
| string | 三选一 | 图片 URL |
| [int,int,int,int] | 否 | 可选目标区域，不传则处理全图 |
返回示例（成功）：
{
"success": true,
"applied_strategy": "upscale_2x",
"strategies_tried": 1,
"result_code": "SUCCESS",
"results": [{"content": "https://example.com", "type": "QRCODE"}],
"image_size": [96, 96],
"resize_factor": 1.0
}返回示例（全部失败）：
{
"success": false,
"applied_strategy": null,
"strategies_tried": 7,
"result_code": "NO_QR_FOUND",
"suggestion": "All 7 enhancement strategies failed to decode a QR code..."
}enhance_and_decode
裁剪指定区域，执行增强操作后再解码。
输入参数：
参数 | 类型 | 必填 | 说明 |
| string | 三选一 | 本地图片绝对路径（推荐——无管道开销） |
| string | 三选一 | Base64 编码的图片 |
| string | 三选一 | 图片 URL |
| [int,int,int,int] 或 [[int,int,int,int], ...] | 是 | 目标区域。单区域传 |
| object[] | 否 | 增强操作列表（见下方），不传则仅裁剪解码 |
增强操作：
操作 | 说明 | 关键参数 |
| 放大区域 |
|
| 锐化边缘 |
|
| 调整对比度 |
|
| 降噪 |
|
结果码说明
result_code 告诉 AI 助手下一步该做什么：
结果码 | 含义 | AI 应该做什么 |
| 解码成功 | 直接使用内容 |
| 解码成功但内容可能有异常 | 检查警告，验证内容 |
| 质量问题，可修复 | 调用 |
| 未检测到二维码 | 告知用户图中没有二维码 |
| 二维码已损坏无法恢复 | 告知用户二维码损坏 |
示例对话
接入后试试对 AI 助手说：
"帮我读一下这张截图里的二维码"
"这个二维码太模糊了，试试增强后再读"
"扫描这张照片里的所有二维码，列出内容"
"这个收据上的码很难扫——能修复一下吗？"
只读模式
设置 READ_ONLY_MODE=true 后，仅保留 decode_qrcode_full。auto_enhance 和 enhance_and_decode 均不可用——AI 只能扫描，不能运行增强管线。
注意：只读模式是行为约束，不是安全边界——三个工具本就不写磁盘、不改动文件，增强操作也只在内存中计算。它控制的是"AI 能调用哪些工具"，而非"进程能否产生副作用"。
适用于审计/日志场景，避免 Agent 对图像执行计算密集的增强重试。
安全说明
通过
image_path读取调用方指定的本地图片（仅扩展名白名单过滤）image_url通过五层 SSRF 防御（单次 DNS 解析 + 连接钉定到校验 IP + hostname 黑名单 + 禁用重定向 + scheme 白名单）保护内网安全stdio 模式下无需 API Key 或认证
设置
MAX_IMAGE_SIZE可限制内存占用日志不记录图片内容和解码数据
项目结构
qr-reader-mcp-server/
├── README.md
├── LICENSE
├── pyproject.toml
├── requirements.txt
├── .env.example
├── .gitignore
├── Dockerfile
├── docker-compose.yml
├── .github/
│ └── workflows/
│ ├── ci.yml
│ └── release.yml
├── docs/
│ ├── setup.md
│ ├── tools.md
│ ├── troubleshooting.md
│ └── prompts.md
├── src/
│ └── qr_reader/
│ ├── __init__.py
│ ├── server.py # MCP 入口 + 三工具注册
│ └── core/
│ ├── __init__.py
│ ├── decoder.py # 二维码解码（pyzbar + OpenCV fallback）
│ ├── ops.py # 统一图像操作层（cv2 / Pillow 双后端）
│ ├── quality.py # 图像质量分析 + ISO 15415 modulation
│ ├── diagnosis.py # 五级结果码分类
│ ├── distortion.py # finder-pattern 几何畸变检测
│ └── url_utils.py # 四层 SSRF 防护
├── tests/
│ ├── test_decoder.py
│ ├── test_diagnosis.py
│ ├── test_e2e.py
│ ├── test_quality.py
│ ├── test_server.py
│ └── test_ssrf.py
├── benchmarks/ # pytest-benchmark 性能回归
├── INSTALL_FOR_AGENT.md # AI Agent 自动安装指南
├── CHANGELOG.md
└── SECURITY.md开源协议
MIT — 详见 LICENSE。
Available Tools
3 toolsauto_enhanceA
Automatically try enhancement strategies to decode a QR code in one call.Tries up to 7 strategies (upscale, sharpen, contrast, denoise, combos) in sequence — returns as soon as one succeeds.Ideal for RETRYABLE results from decode_qrcode_full:no manual bbox estimation or operation selection needed.
ParametersJSON Schema
| Name | Required | Description | Default |
|---|---|---|---|
| bbox | No | Optional target region [x, y, width, height].If omitted, processes the entire image. | |
| image_url | No | Public image URL. | |
| image_path | No | Local image absolute path — preferred when available. | |
| image_base64 | No | Base64-encoded image. |
TDQS
A4.1/5.0
Behavior4/5
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
With no annotations, the description carries the behavioral transparency burden. It discloses the key trait: 'Tries up to 7 strategies (upscale, sharpen, contrast, denoise, combos) in sequence — returns as soon as one succeeds.' This is concrete behavioral description, though it omits failure behavior and any safety implications.
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 two efficient sentences, front-loaded with the core action. Every part adds value: the strategy count, sequence behavior, and ideal use case. No wasted words.
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?
The tool is moderately complex, with no output schema and no annotations. The description explains the strategy sequence but does not clarify the return value format or the fact that an image source is required despite the schema listing zero required parameters. These are notable gaps for a one-call 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 the schema fully documents each parameter. The description adds no additional parameter semantics, and it does not clarify that at least one image source (URL, path, or base64) is required despite all being marked optional in the 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 a specific verb and resource: 'Automatically try enhancement strategies to decode a QR code in one call.' It distinguishes from siblings by noting it tries up to 7 strategies and requires 'no manual bbox estimation or operation selection needed,' which positions it as an automatic alternative.
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 gives clear when-to-use context: 'Ideal for RETRYABLE results from decode_qrcode_full' and 'no manual bbox estimation or operation selection needed.' It implies when not to use (when manual control is desired) but does not explicitly name an alternative like enhance_and_decode.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
decode_qrcode_fullA
Scan the entire image for QR codes and decode them. Returns all detected codes with detailed diagnostics. Agent should decide next step based on result_code:SUCCESS → use content; SUCCESS_WITH_WARNING → check warnings;RETRYABLE → call enhance_and_decode;NO_QR_FOUND / QR_UNRECOVERABLE → inform user.
ParametersJSON Schema
| Name | Required | Description | Default |
|---|---|---|---|
| image_url | No | Public image URL. Use when the image is at a remote location accessible by the server. | |
| image_path | No | Local image absolute path — preferred when available.Pass the path string directly — zero pipe overhead, no timeout. | |
| symbologies | No | Optional whitelist of barcode types to decode. Supported: QRCODE, EAN13, EAN8, CODE128, CODE39, CODABAR, I25, UPC-A, UPC-E, PDF417, DataMatrix, Aztec. Default (empty or omitted) = all types. Use e.g. ['EAN13'] for receipts, ['QRCODE'] for URLs. | |
| image_base64 | No | Base64-encoded image. Use when the image is in memory or when a local path is unavailable. Large images are auto-resized. |
TDQS
A4.4/5.0
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 and discloses key behavioral details: it scans the entire image, returns all detected codes with diagnostics, and defines a result_code decision tree. It could further elaborate on what 'detailed diagnostics' includes, but the provided behavior is sufficient for an agent.
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 compact—three sentences that front-load the purpose, then present a terse decision tree. Every sentence contributes value, with no redundant or filler content.
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?
Given the lack of an output schema and annotations, the description provides a solid decision framework with result_code mappings. It could specify the exact output structure, but the guidance is enough for an agent to act on the tool's results.
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 rich descriptions for each parameter, including usage precedence (e.g., image_path preferred over image_url) and examples for symbologies. The description itself adds no parameter information, but the schema already handles it, so a baseline 3 is appropriate.
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 scans the entire image for QR codes and decodes them, which is a specific verb+resource action. It adds that it returns all detected codes with diagnostics, distinguishing it from simpler or partial decode tools.
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 guides the agent on next steps based on result_code, including a direct alternative: 'RETRYABLE → call enhance_and_decode'. It also instructs to inform the user for NO_QR_FOUND or QR_UNRECOVERABLE, providing clear usage context and alternatives.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
enhance_and_decodeA
Apply enhancement operations to a region of the image, then decode.Enhancement strategy is decided by the Agent based on decode_qrcode_full diagnostics.Supports upscale, sharpen, adjust_contrast, denoise — composable in any order.
ParametersJSON Schema
| Name | Required | Description | Default |
|---|---|---|---|
| bbox | Yes | Target region [x, y, width, height] | |
| image_url | No | Public image URL — use when the image is at a remote location. | |
| image_path | No | Local image absolute path — preferred when available. | |
| operations | No | List of enhancement operations, applied in order.If omitted, crops the region and decodes directly (no enhancement). | |
| image_base64 | No | Base64-encoded image — use when the image is in memory. Large images are auto-resized. |
TDQS
A4.4/5.0
Behavior4/5
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
With no annotations, the description carries the full burden. It discloses composable operations, the fallback when operations are omitted (crops and decodes directly), and that the enhancement strategy is agent-decided based on diagnostics. It does not explicitly state side effects (e.g., whether the original image is modified), but no annotation contradiction exists and the disclosed behavior is meaningful.
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 compact and front-loaded, using three short sentences to convey the core action, strategic context, and supported operations. Every sentence contributes meaningful information without redundancy or filler.
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?
The tool has five parameters and no output schema, but the description covers the main behavioral aspects: enhancement strategy, supported operations, and the optional operations behavior. It does not explain the return value, but given the tool name and siblings, this is acceptable. It is complete enough for an agent to select and invoke it correctly.
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 description coverage is 100%, so the schema already documents parameters. The description adds value by explaining that operations are composable in any order and that omitting the operations list triggers direct decode of the cropped region—information not in the schema for the operations parameter. This exceeds the baseline for high coverage.
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 function: 'Apply enhancement operations to a region of the image, then decode.' It uses a specific verb ('apply', 'decode') and resource ('image region'), and the phrase 'Enhancement strategy is decided by the Agent based on decode_qrcode_full diagnostics' distinguishes it from decode-only and enhance-only siblings.
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?
It provides clear context for when to use the tool: after obtaining diagnostics from decode_qrcode_full and when enhancement is needed. It does not explicitly mention exclusions or alternatives like auto_enhance, so it falls short of a 5 but gives solid guidance.
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.
3 tool updates
v0.2.1- First observed
auto_enhance - First observed
decode_qrcode_full - First observed
enhance_and_decode
TDQS
A4.1/5.0
Disambiguation4/5
The tools are largely distinct: decode_qrcode_full performs initial scanning, enhance_and_decode allows manual enhancement of a region, and auto_enhance automatically tries strategies. The overlap between enhance_and_decode and auto_enhance is clear from descriptions, with one being manual and the other automatic.
Naming Consistency3/5
Tool names follow no consistent pattern: 'decode_qrcode_full' is verb_noun_adjective, 'enhance_and_decode' is two verbs joined by 'and', and 'auto_enhance' is a prefixed verb. They are readable but structurally inconsistent.
Tool Count5/5
Three tools is well-scoped for a QR reader server, covering the core decode step and two enhancement recovery approaches without unnecessary bloat. Each tool has a clear role in the workflow.
Completeness4/5
The surface covers initial decoding and recovery through enhancement, but lacks a direct 'decode region without enhancement' tool. This is a minor gap since the enhancement tools can also handle regions, but a simple region decode would be a natural addition.
Maintenance
ActivitySlowing
ResponsivenessNo issues
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