{
  "protocol": "CCP",
  "version": "v1.0.0",
  "query": "pdf",
  "count": 20,
  "federated": false,
  "results": [
    {
      "id": "pdf-merge-multiple-001",
      "name": "PDF 合并",
      "name_en": "PDF Merge",
      "desc": "合并多个 PDF 文件为一个 PDF 文件",
      "desc_en": "Merge multiple PDF files into a single PDF",
      "input": "多个 PDF 文件路径或 URL",
      "input_en": "Multiple PDF file paths or URLs",
      "output": "合并后的 PDF 文件",
      "output_en": "Merged PDF file",
      "endpoint": "/api/pdf/merge",
      "endpointType": "http",
      "category": "document",
      "provenance": "seed-import",
      "trustSource": 0.6,
      "trustUsage": 0,
      "trustUsageRate": 0,
      "trustSuccess": 0.5,
      "trustRisk": 0.3,
      "trustTime": 0.8,
      "lastUpdated": "2026-09-05",
      "evidence": {
        "count": 0,
        "uncertainty": 0.5
      },
      "features": [
        "批量合并",
        "保持格式",
        "支持加密PDF"
      ],
      "features_en": [
        "Batch merge",
        "Format preservation",
        "Encrypted PDF support"
      ],
      "usageGuide": "提供 PDF 文件列表，按顺序合并",
      "usageGuide_en": "Provide a list of PDF files, merged in order",
      "codeExample": "curl -X POST /api/pdf/merge -F 'files=@a.pdf' -F 'files=@b.pdf'",
      "version": 1,
      "_semanticScore": 0.76,
      "_source": "local"
    },
    {
      "id": "pdf-rotate-pages-001",
      "name": "PDF 页面旋转",
      "name_en": "PDF Page Rotation",
      "desc": "旋转 PDF 中的指定页面",
      "desc_en": "Rotate specific pages in a PDF document",
      "input": "PDF 文件 + 旋转角度（90/180/270）",
      "input_en": "PDF file + rotation angle (90/180/270)",
      "output": "旋转后的 PDF 文件",
      "output_en": "Rotated PDF file",
      "endpoint": "/api/pdf/rotate",
      "endpointType": "http",
      "category": "document",
      "provenance": "seed-import",
      "trustSource": 0.6,
      "trustUsage": 0,
      "trustUsageRate": 0,
      "trustSuccess": 0.5,
      "trustRisk": 0.2,
      "trustTime": 0.8,
      "lastUpdated": "2026-09-05",
      "evidence": {
        "count": 0,
        "uncertainty": 0.5
      },
      "features": [
        "指定页面旋转",
        "支持90/180/270度"
      ],
      "features_en": [
        "Per-page rotation",
        "90/180/270 degree support"
      ],
      "usageGuide": "指定 PDF 文件和旋转角度",
      "usageGuide_en": "Specify PDF file and rotation angle",
      "codeExample": "curl -X POST /api/pdf/rotate -F 'file=@doc.pdf' -F 'angle=90' -F 'pages=1,3,5'",
      "version": 1,
      "_semanticScore": 0.76,
      "_source": "local"
    },
    {
      "id": "pdf-extract-table-002",
      "name": "PDF表格提取",
      "name_en": "PDF Table Extraction",
      "desc": "从PDF文件中提取结构化表格数据，支持多页多表",
      "desc_en": "Extract structured table data from PDFs, supporting multi-page multi-table",
      "input": "PDF文件URL、页码范围（可选）",
      "input_en": "PDF file URL, page range (optional)",
      "output": "JSON格式表格数组，含行列结构",
      "output_en": "JSON array of tables with row/column structure",
      "endpoint": "mcp://pdfplumber-server/extract-tables",
      "endpointType": "mcp",
      "category": "data",
      "provenance": "https://github.com/jsvine/pdfplumber",
      "trustSource": 0.85,
      "trustUsage": 890,
      "trustUsageRate": 0.18,
      "trustSuccess": 0.94,
      "trustRisk": 0.15,
      "trustTime": 0.82,
      "lastUpdated": "2026-08-12",
      "evidence": {
        "count": 890,
        "uncertainty": 0.25
      },
      "features": [
        "多页多表提取",
        "JSON结构化输出",
        "行列自动识别",
        "合并单元格处理"
      ],
      "features_en": [
        "Multi-page/table",
        "JSON output",
        "Auto row/col detect",
        "Merged cells"
      ],
      "usageGuide": "通过MCP协议调用，指定PDF文件路径和页码范围，返回JSON格式的表格数组。",
      "usageGuide_en": "Invoke via MCP protocol, specify PDF path and page range, returns JSON table array.",
      "codeExample": "{\"method\":\"tools/call\",\"params\":{\"name\":\"extract-tables\",\"arguments\":{\"path\":\"/data/report.pdf\",\"pages\":\"1-5\"}}}",
      "version": 1,
      "_semanticScore": 0.76,
      "_source": "local"
    },
    {
      "id": "pdf-extract-text-001",
      "name": "PDF文本提取",
      "name_en": "PDF Text Extraction",
      "desc": "从PDF文件中提取全部文本内容，保留段落结构",
      "desc_en": "Extract all text from PDF files while preserving paragraph structure",
      "input": "PDF文件URL或本地路径",
      "input_en": "PDF file URL or local path",
      "output": "结构化文本字符串，包含页码映射",
      "output_en": "Structured text string with page number mapping",
      "endpoint": "mcp://pymupdf-server/extract-text",
      "endpointType": "mcp",
      "category": "data",
      "provenance": "https://github.com/pymupdf/PyMuPDF",
      "trustSource": 0.92,
      "trustUsage": 1242,
      "trustUsageRate": 0.62,
      "trustSuccess": 0.97,
      "trustRisk": 0.1,
      "trustTime": 0.9,
      "lastUpdated": "2026-08-30",
      "evidence": {
        "count": 1242,
        "uncertainty": 0.24
      },
      "features": [
        "保留段落结构",
        "页码映射",
        "多语言文本支持",
        "批量处理"
      ],
      "features_en": [
        "Preserve paragraphs",
        "Page mapping",
        "Multi-language",
        "Batch processing"
      ],
      "usageGuide": "将PDF文件URL或本地路径作为输入参数传入，返回包含页码映射的结构化文本。",
      "usageGuide_en": "Pass the PDF file URL or local path as input, returns structured text with page mapping.",
      "codeExample": "{\"method\":\"tools/call\",\"params\":{\"name\":\"extract-text\",\"arguments\":{\"path\":\"/data/report.pdf\"}}}",
      "version": 3,
      "_semanticScore": 0.74,
      "_source": "local"
    },
    {
      "id": "image-ocr-004",
      "name": "图片OCR识别",
      "name_en": "Image OCR Recognition",
      "desc": "识别图片中的印刷体和手写体文字，支持多语言",
      "desc_en": "Recognize printed and handwritten text in images, multi-language support",
      "input": "图片文件URL或Base64",
      "input_en": "Image file URL or Base64",
      "output": "识别文本、置信度分数、坐标框",
      "output_en": "Recognized text, confidence scores, bounding boxes",
      "endpoint": "mcp://tesseract-server/recognize",
      "endpointType": "mcp",
      "category": "media",
      "provenance": "https://github.com/tesseract-ocr/tesseract",
      "trustSource": 0.88,
      "trustUsage": 3400,
      "trustUsageRate": 0.68,
      "trustSuccess": 0.96,
      "trustRisk": 0.08,
      "trustTime": 0.9,
      "lastUpdated": "2026-08-18",
      "evidence": {
        "count": 3400,
        "uncertainty": 0.22
      },
      "features": [
        "印刷体识别",
        "手写体识别",
        "多语言支持",
        "坐标框输出"
      ],
      "features_en": [
        "Print recognition",
        "Handwriting",
        "Multi-language",
        "Bounding boxes"
      ],
      "usageGuide": "通过MCP协议传入图片路径，自动识别图片中文字并返回文本、置信度和坐标框信息。",
      "usageGuide_en": "Pass image path via MCP, auto-recognizes text and returns text, confidence, and bounding boxes.",
      "codeExample": "{\"method\":\"tools/call\",\"params\":{\"name\":\"recognize\",\"arguments\":{\"path\":\"/data/scan.png\",\"lang\":\"chi_sim+eng\"}}}",
      "version": 1,
      "_semanticScore": 0.68,
      "_source": "local"
    },
    {
      "id": "image-gen-017",
      "name": "图像生成",
      "name_en": "Image Generation",
      "desc": "根据文本描述生成图像，支持多种风格和尺寸",
      "desc_en": "Generate images from text descriptions with multiple styles and sizes",
      "input": "文本描述、风格、尺寸（可选）",
      "input_en": "Text description, style, size (optional)",
      "output": "生成图像的URL或Base64数据",
      "output_en": "Generated image URL or Base64 data",
      "endpoint": "mcp://stable-diffusion-server/generate",
      "endpointType": "mcp",
      "category": "media",
      "provenance": "https://github.com/CompVis/stable-diffusion",
      "trustSource": 0.65,
      "trustUsage": 8900,
      "trustUsageRate": 0.96,
      "trustSuccess": 0.82,
      "trustRisk": 0.25,
      "trustTime": 0.78,
      "lastUpdated": "2026-08-23",
      "evidence": {
        "count": 8900,
        "uncertainty": 0.2
      },
      "features": [
        "文本到图像",
        "多种风格",
        "尺寸可控",
        "批量生成"
      ],
      "features_en": [
        "Text-to-image",
        "Multiple styles",
        "Size control",
        "Batch generation"
      ],
      "usageGuide": "通过MCP协议传入文本描述，使用Stable Diffusion生成对应图像。",
      "usageGuide_en": "Pass text description via MCP, uses Stable Diffusion to generate matching image.",
      "codeExample": "{\"method\":\"tools/call\",\"params\":{\"name\":\"generate\",\"arguments\":{\"prompt\":\"A serene mountain lake at sunset\",\"style\":\"photorealistic\",\"size\":\"1024x768\"}}}",
      "version": 1,
      "_semanticScore": 0.67,
      "_source": "local"
    },
    {
      "id": "image-ocr-text-001",
      "name": "图片 OCR 文字识别",
      "name_en": "Image OCR Text Recognition",
      "desc": "从图片中提取文字内容，支持中英文",
      "desc_en": "Extract text from images, supporting Chinese and English",
      "input": "图片文件",
      "input_en": "Image file",
      "output": "识别出的文字内容",
      "output_en": "Recognized text content",
      "endpoint": "/api/image/ocr",
      "endpointType": "http",
      "category": "ai",
      "provenance": "seed-import",
      "trustSource": 0.7,
      "trustUsage": 0,
      "trustUsageRate": 0,
      "trustSuccess": 0.6,
      "trustRisk": 0.3,
      "trustTime": 0.7,
      "lastUpdated": "2026-09-05",
      "evidence": {
        "count": 0,
        "uncertainty": 0.5
      },
      "features": [
        "中英文识别",
        "表格识别",
        "手写体识别"
      ],
      "features_en": [
        "Chinese/English recognition",
        "Table recognition",
        "Handwriting recognition"
      ],
      "usageGuide": "上传图片文件，返回识别文字",
      "usageGuide_en": "Upload image file, returns recognized text",
      "codeExample": "curl -X POST /api/image/ocr -F 'file=@scan.jpg' -F 'lang=zh'",
      "version": 1,
      "_semanticScore": 0.66,
      "_source": "local"
    },
    {
      "id": "image-resize-batch-001",
      "name": "图片批量缩放",
      "name_en": "Batch Image Resize",
      "desc": "批量调整图片尺寸，支持多种输出格式",
      "desc_en": "Batch resize images with multiple output format support",
      "input": "图片文件列表 + 目标尺寸",
      "input_en": "Image file list + target dimensions",
      "output": "缩放后的图片",
      "output_en": "Resized images",
      "endpoint": "/api/image/resize",
      "endpointType": "http",
      "category": "media",
      "provenance": "seed-import",
      "trustSource": 0.6,
      "trustUsage": 0,
      "trustUsageRate": 0,
      "trustSuccess": 0.5,
      "trustRisk": 0.2,
      "trustTime": 0.8,
      "lastUpdated": "2026-09-05",
      "evidence": {
        "count": 0,
        "uncertainty": 0.5
      },
      "features": [
        "批量处理",
        "保持比例",
        "PNG/JPG/WebP输出"
      ],
      "features_en": [
        "Batch processing",
        "Aspect ratio preservation",
        "PNG/JPG/WebP output"
      ],
      "usageGuide": "上传图片并指定目标宽度/高度",
      "usageGuide_en": "Upload images and specify target width/height",
      "codeExample": "curl -X POST /api/image/resize -F 'file=@photo.jpg' -F 'width=800' -F 'height=600'",
      "version": 1,
      "_semanticScore": 0.65,
      "_source": "local"
    },
    {
      "id": "doc-diff-010",
      "name": "文档差异对比",
      "name_en": "Document Diff",
      "desc": "对比两份文本的差异，生成行级和字符级变更标记",
      "desc_en": "Compare two texts and generate line-level and character-level diff",
      "input": "原文本、修改后文本",
      "input_en": "Original text, modified text",
      "output": "差异标记文本、变更统计",
      "output_en": "Diff-marked text, change statistics",
      "endpoint": "mcp://difftastic-server/diff",
      "endpointType": "mcp",
      "category": "data",
      "provenance": "https://github.com/Wilfred/difftastic",
      "trustSource": 0.86,
      "trustUsage": 1500,
      "trustUsageRate": 0.3,
      "trustSuccess": 0.95,
      "trustRisk": 0.09,
      "trustTime": 0.84,
      "lastUpdated": "2026-08-08",
      "evidence": {
        "count": 1500,
        "uncertainty": 0.24
      },
      "features": [
        "行级对比",
        "字符级对比",
        "变更统计",
        "统一diff格式"
      ],
      "features_en": [
        "Line-level diff",
        "Char-level diff",
        "Change stats",
        "Unified diff format"
      ],
      "usageGuide": "通过MCP协议传入原文本和修改后文本，生成行级和字符级差异标记。",
      "usageGuide_en": "Pass original and modified text via MCP, generates line-level and character-level diff.",
      "codeExample": "{\"method\":\"tools/call\",\"params\":{\"name\":\"diff\",\"arguments\":{\"original\":\"Hello World\",\"modified\":\"Hello SkillMesh\"}}}",
      "version": 1,
      "_semanticScore": 0.64,
      "_source": "local"
    },
    {
      "id": "csv-json-007",
      "name": "数据格式转换",
      "name_en": "Data Format Converter",
      "desc": "在CSV、JSON、XML、YAML之间双向转换，保留数据类型",
      "desc_en": "Bidirectional conversion between CSV, JSON, XML, YAML",
      "input": "源数据字符串、源格式、目标格式",
      "input_en": "Source data string, source format, target format",
      "output": "转换后的数据字符串",
      "output_en": "Converted data string",
      "endpoint": "mcp://sqlite-utils/convert",
      "endpointType": "mcp",
      "category": "data",
      "provenance": "https://github.com/simonw/sqlite-utils",
      "trustSource": 0.9,
      "trustUsage": 4200,
      "trustUsageRate": 0.84,
      "trustSuccess": 0.98,
      "trustRisk": 0.03,
      "trustTime": 0.92,
      "lastUpdated": "2026-08-15",
      "evidence": {
        "count": 4200,
        "uncertainty": 0.22
      },
      "features": [
        "CSV/JSON/XML/YAML",
        "双向转换",
        "类型保留",
        "嵌套结构支持"
      ],
      "features_en": [
        "CSV/JSON/XML/YAML",
        "Bidirectional",
        "Type preservation",
        "Nested structures"
      ],
      "usageGuide": "通过MCP协议传入源数据、源格式和目标格式，自动完成双向转换。",
      "usageGuide_en": "MCP call with source data, source format, and target format for auto bidirectional conversion.",
      "codeExample": "{\"method\":\"tools/call\",\"params\":{\"name\":\"convert\",\"arguments\":{\"data\":\"...\",\"from\":\"csv\",\"to\":\"json\"}}}",
      "version": 1,
      "_semanticScore": 0.64,
      "_source": "local"
    },
    {
      "id": "translate-014",
      "name": "文本翻译",
      "name_en": "Text Translation",
      "desc": "在100+语言之间进行高质量文本翻译，保留格式",
      "desc_en": "High-quality text translation between 100+ languages, preserving format",
      "input": "源文本、源语言、目标语言",
      "input_en": "Source text, source language, target language",
      "output": "翻译后文本、置信度分数",
      "output_en": "Translated text, confidence score",
      "endpoint": "prompt://translate?from={lang}&to={lang}",
      "endpointType": "prompt",
      "category": "language",
      "provenance": "https://github.com/argosopentech/argos-translate",
      "trustSource": 0.74,
      "trustUsage": 7800,
      "trustUsageRate": 0.94,
      "trustSuccess": 0.88,
      "trustRisk": 0.07,
      "trustTime": 0.83,
      "lastUpdated": "2026-08-19",
      "evidence": {
        "count": 7800,
        "uncertainty": 0.2
      },
      "features": [
        "100+语言",
        "格式保留",
        "置信度输出",
        "批量翻译"
      ],
      "features_en": [
        "100+ languages",
        "Format preservation",
        "Confidence score",
        "Batch translation"
      ],
      "usageGuide": "通过Prompt协议传入源文本和语言对，自动翻译并返回翻译结果和置信度。",
      "usageGuide_en": "Pass source text and language pair via Prompt, returns translation and confidence score.",
      "codeExample": "{\"prompt\":\"translate\",\"text\":\"Hello, how are you?\",\"from\":\"en\",\"to\":\"zh\"}",
      "version": 1,
      "_semanticScore": 0.63,
      "_source": "local"
    },
    {
      "id": "audio-transcribe-speech-001",
      "name": "语音转文字",
      "name_en": "Speech to Text",
      "desc": "将音频文件中的语音转录为文字",
      "desc_en": "Transcribe speech from audio files to text",
      "input": "音频文件（mp3/wav/m4a）",
      "input_en": "Audio file (mp3/wav/m4a)",
      "output": "转录文字",
      "output_en": "Transcribed text",
      "endpoint": "/api/audio/transcribe",
      "endpointType": "http",
      "category": "ai",
      "provenance": "seed-import",
      "trustSource": 0.7,
      "trustUsage": 0,
      "trustUsageRate": 0,
      "trustSuccess": 0.6,
      "trustRisk": 0.3,
      "trustTime": 0.6,
      "lastUpdated": "2026-09-05",
      "evidence": {
        "count": 0,
        "uncertainty": 0.5
      },
      "features": [
        "多语言支持",
        "说话人识别",
        "时间戳标记"
      ],
      "features_en": [
        "Multi-language",
        "Speaker diarization",
        "Timestamp markers"
      ],
      "usageGuide": "上传音频文件，指定语言",
      "usageGuide_en": "Upload audio file, specify language",
      "codeExample": "curl -X POST /api/audio/transcribe -F 'file=@recording.mp3' -F 'lang=zh'",
      "version": 1,
      "_semanticScore": 0.63,
      "_source": "local"
    },
    {
      "id": "web-search-011",
      "name": "联网搜索",
      "name_en": "Web Search",
      "desc": "执行互联网搜索，返回结构化结果含标题、摘要和URL",
      "desc_en": "Execute internet search, returning structured results with title, snippet, and URL",
      "input": "搜索关键词、结果数量（可选）",
      "input_en": "Search keywords, result count (optional)",
      "output": "搜索结果列表，含标题、摘要、URL",
      "output_en": "Search result list with title, snippet, URL",
      "endpoint": "mcp://searxng-server/search",
      "endpointType": "mcp",
      "category": "ai",
      "provenance": "https://github.com/searxng/searxng",
      "trustSource": 0.76,
      "trustUsage": 6800,
      "trustUsageRate": 0.92,
      "trustSuccess": 0.9,
      "trustRisk": 0.18,
      "trustTime": 0.87,
      "lastUpdated": "2026-08-26",
      "evidence": {
        "count": 6800,
        "uncertainty": 0.21
      },
      "features": [
        "多引擎聚合",
        "隐私保护",
        "结构化结果",
        "时间范围筛选"
      ],
      "features_en": [
        "Multi-engine",
        "Privacy",
        "Structured results",
        "Time range filter"
      ],
      "usageGuide": "通过MCP协议传入搜索关键词，使用SearXNG多引擎聚合搜索，返回结构化结果。",
      "usageGuide_en": "Pass search keywords via MCP, uses SearXNG multi-engine meta-search, returns structured results.",
      "codeExample": "{\"method\":\"tools/call\",\"params\":{\"name\":\"search\",\"arguments\":{\"q\":\"latest AI research 2026\",\"count\":10}}}",
      "version": 1,
      "_semanticScore": 0.63,
      "_source": "local"
    },
    {
      "id": "file-read-012",
      "name": "文件读取",
      "name_en": "File Reader",
      "desc": "读取本地文件系统中任意文本文件，支持多种编码格式",
      "desc_en": "Read any text file from local filesystem with multi-encoding support",
      "input": "文件路径、编码格式（可选）",
      "input_en": "File path, encoding (optional)",
      "output": "文件文本内容、行数、编码信息",
      "output_en": "File text content, line count, encoding info",
      "endpoint": "mcp://filesystem-server/read",
      "endpointType": "mcp",
      "category": "data",
      "provenance": "https://github.com/modelcontextprotocol/servers",
      "trustSource": 0.94,
      "trustUsage": 9200,
      "trustUsageRate": 0.95,
      "trustSuccess": 0.99,
      "trustRisk": 0.04,
      "trustTime": 0.93,
      "lastUpdated": "2026-08-24",
      "evidence": {
        "count": 9200,
        "uncertainty": 0.2
      },
      "features": [
        "多编码支持",
        "大文件流式读取",
        "行号输出",
        "元数据提取"
      ],
      "features_en": [
        "Multi-encoding",
        "Streaming",
        "Line numbers",
        "Metadata"
      ],
      "usageGuide": "通过MCP协议传入文件路径，自动检测编码并读取文本内容，返回带行号的内容。",
      "usageGuide_en": "Pass file path via MCP, auto-detects encoding and reads text content with line numbers.",
      "codeExample": "{\"method\":\"tools/call\",\"params\":{\"name\":\"read\",\"arguments\":{\"path\":\"/project/src/main.py\"}}}",
      "version": 1,
      "_semanticScore": 0.62,
      "_source": "local"
    },
    {
      "id": "email-send-018",
      "name": "邮件发送",
      "name_en": "Email Sender",
      "desc": "通过SMTP或API发送邮件，支持HTML模板和附件",
      "desc_en": "Send emails via SMTP or API with HTML templates and attachments",
      "input": "收件人、主题、正文、附件（可选）",
      "input_en": "Recipient, subject, body, attachments (optional)",
      "output": "发送状态、消息ID",
      "output_en": "Send status, message ID",
      "endpoint": "mcp://resend-server/send",
      "endpointType": "mcp",
      "category": "data",
      "provenance": "https://github.com/resend/resend-node",
      "trustSource": 0.83,
      "trustUsage": 2600,
      "trustUsageRate": 0.52,
      "trustSuccess": 0.96,
      "trustRisk": 0.14,
      "trustTime": 0.88,
      "lastUpdated": "2026-08-17",
      "evidence": {
        "count": 2600,
        "uncertainty": 0.23
      },
      "features": [
        "HTML模板",
        "附件支持",
        "批量发送",
        "发送状态追踪"
      ],
      "features_en": [
        "HTML templates",
        "Attachments",
        "Batch send",
        "Status tracking"
      ],
      "usageGuide": "通过MCP协议传入收件人、主题和正文，使用Resend发送邮件并返回状态。",
      "usageGuide_en": "Pass recipient, subject, and body via MCP, sends email via Resend and returns status.",
      "codeExample": "{\"method\":\"tools/call\",\"params\":{\"name\":\"send\",\"arguments\":{\"to\":\"user@example.com\",\"subject\":\"Hello\",\"body\":\"<h1>Welcome</h1>\"}}}",
      "version": 1,
      "_semanticScore": 0.62,
      "_source": "local"
    },
    {
      "id": "web-scrape-003",
      "name": "网页内容抓取",
      "name_en": "Web Content Scraper",
      "desc": "抓取任意网页的可见文本内容，自动去除广告和导航",
      "desc_en": "Scrape visible text from any webpage, auto-removing ads and navigation",
      "input": "目标网页URL",
      "input_en": "Target webpage URL",
      "output": "纯文本内容、标题、元数据",
      "output_en": "Plain text content, title, metadata",
      "endpoint": "mcp://crawlee-server/scrape",
      "endpointType": "mcp",
      "category": "data",
      "provenance": "https://github.com/apify/crawlee",
      "trustSource": 0.78,
      "trustUsage": 2100,
      "trustUsageRate": 0.42,
      "trustSuccess": 0.91,
      "trustRisk": 0.22,
      "trustTime": 0.75,
      "lastUpdated": "2026-08-05",
      "evidence": {
        "count": 2100,
        "uncertainty": 0.23
      },
      "features": [
        "自动去广告",
        "导航栏过滤",
        "元数据提取",
        "JavaScript渲染支持"
      ],
      "features_en": [
        "Auto ad removal",
        "Nav filtering",
        "Metadata extraction",
        "JS rendering"
      ],
      "usageGuide": "通过MCP协议传入目标URL，自动抓取页面可见文本并去除广告和导航栏。",
      "usageGuide_en": "Send MCP call with target URL, auto-scrapes visible text removing ads and nav bars.",
      "codeExample": "{\"method\":\"tools/call\",\"params\":{\"name\":\"scrape\",\"arguments\":{\"url\":\"https://example.com/article\"}}}",
      "version": 1,
      "_semanticScore": 0.61,
      "_source": "local"
    },
    {
      "id": "text-summarize-005",
      "name": "文本摘要生成",
      "name_en": "Text Summarization",
      "desc": "对长文本生成提取式和生成式摘要，支持指定长度",
      "desc_en": "Generate extractive and abstractive summaries for long text",
      "input": "长文本字符串、期望摘要长度",
      "input_en": "Long text string, desired summary length",
      "output": "摘要文本、关键句索引",
      "output_en": "Summary text, key sentence indices",
      "endpoint": "prompt://summarize?max_length={n}",
      "endpointType": "prompt",
      "category": "language",
      "provenance": "https://github.com/huggingface/transformers",
      "trustSource": 0.72,
      "trustUsage": 5600,
      "trustUsageRate": 0.9,
      "trustSuccess": 0.89,
      "trustRisk": 0.05,
      "trustTime": 0.85,
      "lastUpdated": "2026-08-22",
      "evidence": {
        "count": 5600,
        "uncertainty": 0.21
      },
      "features": [
        "提取式摘要",
        "生成式摘要",
        "长度可控",
        "关键句索引"
      ],
      "features_en": [
        "Extractive summary",
        "Abstractive summary",
        "Length control",
        "Key sentence index"
      ],
      "usageGuide": "通过Prompt协议传入长文本和期望长度，生成两种模式的摘要并标记关键句。",
      "usageGuide_en": "Pass long text and desired length via Prompt protocol, generates two-mode summaries with key sentences.",
      "codeExample": "{\"prompt\":\"summarize\",\"text\":\"Long article text here...\",\"max_length\":200}",
      "version": 1,
      "_semanticScore": 0.61,
      "_source": "local"
    },
    {
      "id": "calendar-019",
      "name": "日历事件创建",
      "name_en": "Calendar Event Creator",
      "desc": "创建和管理日历事件，支持iCal格式和重复规则",
      "desc_en": "Create and manage calendar events with iCal format and recurrence rules",
      "input": "事件标题、时间、地点、重复规则（可选）",
      "input_en": "Event title, time, location, recurrence (optional)",
      "output": "iCal事件对象、事件ID",
      "output_en": "iCal event object, event ID",
      "endpoint": "mcp://icalendar-server/create",
      "endpointType": "mcp",
      "category": "data",
      "provenance": "https://github.com/icalendar/icalendar",
      "trustSource": 0.79,
      "trustUsage": 980,
      "trustUsageRate": 0.2,
      "trustSuccess": 0.94,
      "trustRisk": 0.11,
      "trustTime": 0.81,
      "lastUpdated": "2026-08-02",
      "evidence": {
        "count": 980,
        "uncertainty": 0.25
      },
      "features": [
        "iCal标准",
        "重复规则",
        "多时区",
        "提醒设置"
      ],
      "features_en": [
        "iCal standard",
        "Recurrence",
        "Multi-timezone",
        "Reminders"
      ],
      "usageGuide": "通过MCP协议传入事件信息，生成标准iCal格式的日历事件。",
      "usageGuide_en": "Pass event info via MCP, generates standard iCal format calendar event.",
      "codeExample": "{\"method\":\"tools/call\",\"params\":{\"name\":\"create\",\"arguments\":{\"title\":\"Team Meeting\",\"start\":\"2026-09-01T10:00:00\",\"end\":\"2026-09-01T11:00:00\"}}}",
      "version": 1,
      "_semanticScore": 0.61,
      "_source": "local"
    },
    {
      "id": "text-summarize-zh-001",
      "name": "中文文本摘要",
      "name_en": "Chinese Text Summarization",
      "desc": "对中文长文本进行自动摘要生成",
      "desc_en": "Auto-generate summaries for long Chinese text",
      "input": "中文文本内容",
      "input_en": "Chinese text content",
      "output": "摘要文本",
      "output_en": "Summary text",
      "endpoint": "/api/text/summarize",
      "endpointType": "http",
      "category": "ai",
      "provenance": "seed-import",
      "trustSource": 0.7,
      "trustUsage": 0,
      "trustUsageRate": 0,
      "trustSuccess": 0.6,
      "trustRisk": 0.4,
      "trustTime": 0.7,
      "lastUpdated": "2026-09-05",
      "evidence": {
        "count": 0,
        "uncertainty": 0.5
      },
      "features": [
        "抽取式摘要",
        "生成式摘要",
        "长度可控"
      ],
      "features_en": [
        "Extractive summary",
        "Abstractive summary",
        "Controllable length"
      ],
      "usageGuide": "传入文本和期望摘要长度",
      "usageGuide_en": "Pass text and desired summary length",
      "codeExample": "curl -X POST /api/text/summarize -d '{\"text\":\"长文本...\",\"max_length\":200}'",
      "version": 1,
      "_semanticScore": 0.6,
      "_source": "local"
    },
    {
      "id": "sentiment-015",
      "name": "情感分析",
      "name_en": "Sentiment Analysis",
      "desc": "分析文本的情感极性（正面/负面/中性）及强度分数",
      "desc_en": "Analyze text sentiment polarity (positive/negative/neutral) with intensity",
      "input": "待分析文本",
      "input_en": "Text to analyze",
      "output": "情感标签、极性分数、情绪分布",
      "output_en": "Sentiment label, polarity score, emotion distribution",
      "endpoint": "mcp://vader-server/analyze",
      "endpointType": "mcp",
      "category": "ai",
      "provenance": "https://github.com/cjhutto/vaderSentiment",
      "trustSource": 0.82,
      "trustUsage": 5600,
      "trustUsageRate": 0.9,
      "trustSuccess": 0.92,
      "trustRisk": 0.05,
      "trustTime": 0.86,
      "lastUpdated": "2026-08-10",
      "evidence": {
        "count": 5600,
        "uncertainty": 0.21
      },
      "features": [
        "正/负/中性",
        "强度分数",
        "情绪分布",
        "多语言"
      ],
      "features_en": [
        "Pos/Neg/Neutral",
        "Intensity",
        "Emotion distribution",
        "Multi-language"
      ],
      "usageGuide": "通过MCP协议传入文本，使用VADER情感词典分析情感极性和强度分数。",
      "usageGuide_en": "Pass text via MCP, uses VADER sentiment lexicon to analyze polarity and intensity.",
      "codeExample": "{\"method\":\"tools/call\",\"params\":{\"name\":\"analyze\",\"arguments\":{\"text\":\"This product is amazing! Highly recommended.\"}}}",
      "version": 1,
      "_semanticScore": 0.59,
      "_source": "local"
    }
  ]
}