{"$schema":"https://wellknown.network/schemas/agent-record-v1.json","schemaVersion":"1","id":"ag_wyt4898krech","handle":"iflow-mcp-mcp-simple-tool","url":"https://wellknown.network/agents/iflow-mcp-mcp-simple-tool","links":{"self":"https://wellknown.network/agents/iflow-mcp-mcp-simple-tool/record.json","html":"https://wellknown.network/agents/iflow-mcp-mcp-simple-tool","markdown":"https://wellknown.network/agents/iflow-mcp-mcp-simple-tool/record.md","api":"https://wellknown.network/api/v1/agents/iflow-mcp-mcp-simple-tool","status":"https://wellknown.network/api/v1/agents/iflow-mcp-mcp-simple-tool/status","claim":"https://wellknown.network/agents/iflow-mcp-mcp-simple-tool/claim","claimApi":"https://wellknown.network/api/v1/claims","claimDescriptor":"https://wellknown.network/agents/iflow-mcp-mcp-simple-tool/claim.json","badge":"https://wellknown.network/agents/iflow-mcp-mcp-simple-tool/badge.svg","openapi":"https://wellknown.network/openapi.json","history":"https://wellknown.network/api/v1/agents/iflow-mcp-mcp-simple-tool/history","tools":"https://wellknown.network/api/v1/agents/iflow-mcp-mcp-simple-tool/tools"},"ard":{"identifier":"urn:air::server:iflow-mcp-mcp-simple-tool","type":"application/mcp-server-card+json"},"kind":"mcp_server","declared":{"name":"iflow-mcp_mcp-simple-tool","summary":"MCP工具集合，包含文件处理和网页获取功能","description":"# MCP开发框架\n[![Verified on MseeP](https://mseep.ai/badge.svg)](https://mseep.ai/app/34780cde-ee17-4a7b-b9ee-356f41fc9e77) [![smithery badge](https://smithery.ai/badge/@aigo666/mcp-framework)](https://smithery.ai/server/@aigo666/mcp-framework)\n\n一个强大的MCP（Model Context Protocol）开发框架，用于创建与大语言模型交互的自定义工具。该框架提供了一套完整的工具集，可以轻松地扩展Cursor IDE的功能，实现网页内容获取、文件处理（PDF、Word、Excel、CSV、Markdown）以及AI对话等高级功能。它具有强大的MCP工具扩展能力，使开发者能够快速构建和集成各种自定义工具。\n\n<a href=\"https://glama.ai/mcp/servers/@aigo666/mcp-framework\">\n  <img width=\"380\" height=\"200\" src=\"https://glama.ai/mcp/servers/@aigo666/mcp-framework/badge\" />\n</a>\n\n<details>\n<summary>🔥 最新特性：文档图片内容显示与理解</summary>\n\n最新版本现在支持在PDF和Word文档处理中，直接返回原始图片内容并进行OCR识别，使大语言模型能够同时理解文档中的文本和图像内容：\n\n- **图片内容直接显示**：文档中的图表、图像等可以直接在对话中显示，无需额外工具\n- **OCR文本识别**：自动提取图片中的文字内容，支持中英文多语言\n- **图片内容理解**：大模型可以\"看到\"文档中的图片，并基于图片内容进行分析和回答\n- **完整文档内容返回**：真正实现文档的全内容理解，包括文本、表格和图像\n\n这使得AI模型能够更全面地理解和分析文档内容，特别是对于包含图表、表单、流程图或其他可视化信息的文档尤为有价值。\n</details>\n\n## 主要功能\n\n<details>\n<summary>点击展开查看框架提供的核心功能</summary>\n\n本框架提供了以下核心功能：\n\n### 1. 综合文件处理\n\n使用`parse_file`工具可以自动识别文件类型并选择合适的处理方式，支持PDF、Word、Excel、CSV和Markdown文件。\n\n- **用法**: `parse_file /path/to/document`\n- **支持格式**: \n  - PDF文件 (.pdf)\n  - Word文档 (.doc, .docx)\n  - Excel文件 (.xls, .xlsx, .xlsm)\n  - CSV文件 (.csv)\n  - Markdown文件 (.md)\n- **参数**: `file_path` - 文件的本地路径\n- **返回**: 根据文件类型返回相应的处理结果\n\n### 2. PDF文档处理\n\n使用`parse_pdf`工具可以处理PDF文档，支持两种处理模式：\n\n- **用法**: `parse_pdf /path/to/document.pdf [mode]`\n- **参数**: \n  - `file_path` - PDF文件的本地路径\n  - `mode` - 处理模式（可选）：\n    - `quick` - 快速预览模式，仅提取文本内容\n    - `full` - 完整解析模式，提取文本、图片内容和OCR文本（默认）\n- **返回**: \n  - 快速预览模式：文档的文本内容\n  - 完整解析模式：文档的文本内容、原始图片和OCR识别结果\n\n### 3. Word文档解析\n\n使用`parse_word`工具可以解析Word文档，提取文本、表格和图片信息。\n\n- **用法**: `parse_word /path/to/document.docx`\n- **功能**: 解析Word文档并提取文本内容、表格和图片\n- **参数**: `file_path` - Word文档的本地路径\n- **返回**: 文档的文本内容、表格和原始图片\n- **特点**: 同时提供文档内嵌图像的显示和分析功能\n\n### 4. Excel文件处理\n\n使用`parse_excel`工具可以解析Excel文件，提供完整的表格数据和结构信息。\n\n- **用法**: `parse_excel /path/to/spreadsheet.xlsx`\n- **功能**: 解析Excel文件的所有工作表\n-…","publisher":{"name":"aigo666","url":null},"homepage":null,"repository":null,"version":"1.2.0","license":"MIT","protocols":["mcp"],"tags":["automation","csv","excel","fetch","llm","mcp","pdf","web","word"],"pricing":null,"endpoints":[{"url":"pypi:iflow-mcp_mcp-simple-tool","type":"package_pypi","auth":null,"probeable":false}],"skills":null,"tools":null,"extra":null,"attribution":{"kind":"pypi","name":"pypi","license":"pypi","summary":"pypi","version":"pypi","description":"pypi","publisherName":"pypi"}},"derived":{"capabilities":[{"slug":"documents.spreadsheets","name":"Spreadsheets","confidence":1,"provenance":"declared"},{"slug":"research.web","name":"Deep Web Research","confidence":1,"provenance":"declared"}],"categories":["documents","research"],"language":"zh"},"observed":{"status":"unknown","statusReason":"Distributed as a package to run locally; no network endpoint to check.","lastOkAt":null,"lastProbedAt":null,"statusComputedAt":null,"reliability30d":null,"latestObservations":[],"tools":null,"package":{"name":"iflow-mcp_mcp-simple-tool","registry":"pypi","observedAt":"2026-10-28T05:24:12.322Z","publishedAt":"2025-11-20T01:06:28.657210Z","latestVersion":"1.2.0"},"toolSurface":null,"endpointFacts":[]},"verification":{"claimed":false,"claimedAt":null,"proofs":[]},"provenance":{"sources":[{"source":"pypi","key":"iflow-mcp_mcp-simple-tool","url":"https://pypi.org/project/iflow-mcp_mcp-simple-tool/","firstSeenAt":"2026-09-09T20:23:56.986Z","fetchedAt":"2026-10-28T05:23:20.526Z","normalizedAt":"2026-10-28T05:23:20.526Z"}]},"firstSeenAt":"2026-09-09T20:23:56.986Z","updatedAt":"2026-10-28T05:24:12.322Z"}