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MaaMCP_CC

專為 Claude Code 打造的 MaaFramework Pipeline 開發工具

這是什麼?

MaaMCP_CC 是一個 MCP (Model Context Protocol) 伺服器,讓 Claude Code 能夠直接操控 Android 模擬器或 Windows 視窗,專門優化 MaaFramework Pipeline 開發流程。

與 MaaMCP 的差異

MaaMCP MaaMCP_CC
設計對象 Cursor、LM Studio 等 AI Agent Claude Code
截圖方式 OCR 回傳純文字 回傳圖片,Claude 直接用眼睛看
座標定位 靠 OCR 猜座標 screenshot_with_grid 格線精確定位
模板製作 手動裁圖 crop_template 一鍵裁切 + 產生 pipeline 用法
識別測試 寫檔案 → 跑 Pipeline → 看 log test_recognition 即時測試
連接流程 多個工具來回呼叫 單一工具完成連接 + 載入資源
手動操作 需要另外控制裝置 內建 click / swipe
資源重載 必須重新連接 reload_resource 熱重載
執行結果 成功 / 失敗 逐節點詳細報告 + 完成時截圖

安裝

前置需求

步驟一:安裝套件

pip install maamcp-cc

步驟二:在你的 MaaFramework 專案中加入 MCP 設定

在專案根目錄建立 .mcp.json:

{
  "mcpServers": {
    "MaaMCP_CC": {
      "command": "maamcp-cc"
    }
  }
}

用 Claude Code 開啟專案資料夾,會自動偵測 .mcp.json 並詢問是否啟用。

步驟三:確認安裝成功

claude mcp list

看到 MaaMCP_CC: ... ✓ Connected 即為成功。


工具一覽

設備管理

工具 說明
list_devices 掃描所有 ADB 設備和 Windows 視窗
connect_adb 連接 Android 設備 + 載入資源包
connect_window 連接 Windows 視窗(後台操作)
get_session_info 查看連接狀態

畫面觀察

工具 說明
screenshot 擷取畫面(支援圖片模式 / OCR 純文字模式)
screenshot_with_grid 帶座標格線的截圖,精確定位 UI 元素

開發輔助

工具 說明
crop_template 裁切模板圖,自動存檔 + 產生 pipeline JSON
test_recognition 即時測試識別節點,不需寫檔
reload_resource 修改 pipeline JSON 後熱重載

裝置操作

工具 說明
click 點擊指定座標
swipe 滑動操作

任務執行

工具 說明
run_task 執行 Pipeline 任務,回傳逐節點報告
list_tasks 列出所有可用的 Pipeline 節點
stop_task 停止執行中的任務

開發工作流程

1. list_devices              → 查看有哪些設備
2. connect_adb / window      → 連接設備 + 載入資源
3. screenshot_with_grid      → 帶格線截圖,確定 UI 元素座標
4. crop_template             → 裁切模板圖(自動產生 pipeline 用法)
5. test_recognition          → 即時測試識別條件(可重複調整)
6. 修改 Pipeline JSON
7. reload_resource           → 熱重載修改後的資源
8. run_task                  → 驗證結果

Token 管理策略

圖片會消耗較多 token,可依情況選擇:

方法 Token 消耗 適用場景
screenshot(include_image=False) 極低 只需要文字資訊
screenshot() 中等 需要視覺分析
screenshot_with_grid() 中等 定位 UI 元素座標

圖片已自動壓縮(縮放至 960px + JPEG 60% 品質)。


重點工具詳解

screenshot_with_grid

在截圖上疊加座標格線,讓 Claude 能精確讀出每個 UI 元素的座標。

screenshot_with_grid(grid_step=100)

搭配 crop_template 使用:先看格線確定座標 → 裁切模板。

crop_template

從截圖裁切一塊區域作為模板圖,自動儲存到資源包的 image/ 目錄。

crop_template(x=1150, y=25, width=80, height=30, save_name="skip_button.png")

回傳值直接包含可貼進 Pipeline JSON 的識別定義:

{
  "recognition": "TemplateMatch",
  "template": ["skip_button.png"],
  "roi": [1150, 25, 80, 30]
}

預設使用上一次截圖的快取(與 screenshot_with_grid 看到的是同一張),設定 new_screenshot=True 可拍新的。

test_recognition

即時測試識別節點,不需要寫入任何 Pipeline 檔案。

OCR 範例:

{
  "recognition_type": "OCR",
  "params": { "expected": ["確認", "OK"], "roi": [500, 600, 400, 100] }
}

TemplateMatch 範例:

{
  "recognition_type": "TemplateMatch",
  "params": { "template": ["skip_button.png"], "threshold": [0.8] }
}

回傳標記了命中位置的截圖(命中為綠框,未命中顯示 MISS)。

reload_resource

修改 Pipeline JSON 或新增模板圖片後,呼叫即可生效,不需要重新連接設備。


技術細節

  • 語言:Python 3.10+
  • 依賴:FastMCP、MaaFw、OpenCV
  • 授權:MIT

相關專案

  • MaaFramework — 底層自動化框架
  • MaaMCP — 原版 MCP(適合 Cursor 等工具)

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