parse_csv
Extract and parse CSV file contents, supporting multiple encoding formats, for efficient data processing and integration within the MCP Development Framework.
Instructions
解析CSV文件内容，支持各种编码格式
Input Schema
TableJSON Schema
| Name | Required | Description | Default |
|---|---|---|---|
| encoding | No | 文件编码格式，例如'utf-8'、'gbk'等，默认自动检测 | |
| file_path | Yes | CSV文件的本地路径，例如'/path/to/data.csv' |
Implementation Reference
- mcp_tool/tools/csv_tool.py:12-19 (registration)Tool registration using @ToolRegistry.register decorator on CsvTool class, setting name to 'parse_csv' and description.@ToolRegistry.register class CsvTool(BaseTool): """ CSV文件处理工具，用于解析CSV文件内容 """ name = "parse_csv" description = "解析CSV文件内容，支持各种编码格式"
- mcp_tool/tools/csv_tool.py:20-33 (schema)Input schema specifying required 'file_path' parameter and optional 'encoding' for the CSV parsing tool.input_schema = { "type": "object", "required": ["file_path"], "properties": { "file_path": { "type": "string", "description": "CSV文件的本地路径，例如'/path/to/data.csv'", }, "encoding": { "type": "string", "description": "文件编码格式，例如'utf-8'、'gbk'等，默认自动检测", } }, }
- mcp_tool/tools/csv_tool.py:35-110 (handler)The main handler function 'execute' that validates input, processes file path, detects encoding, reads CSV with pandas, generates summary info, preview, stats, and returns formatted text content or error.async def execute(self, arguments: Dict[str, Any]) -> List[types.TextContent]: """ 解析CSV文件内容 Args: arguments: 参数字典，必须包含'file_path'键，可选'encoding'键 Returns: 解析结果列表 """ if "file_path" not in arguments: return [types.TextContent( type="text", text="错误: 缺少必要参数 'file_path'" )] file_path = arguments["file_path"] # 处理文件路径，支持挂载目录的转换 file_path = self.process_file_path(file_path) if not os.path.exists(file_path): return [types.TextContent( type="text", text=f"错误: 文件不存在: {file_path}" )] try: # 尝试自动检测编码 encoding = arguments.get("encoding", None) if encoding is None: try: import chardet with open(file_path, 'rb') as f: raw_data = f.read() encoding = chardet.detect(raw_data)['encoding'] except ImportError: encoding = 'utf-8' # 如果没有chardet，默认使用utf-8 # 读取CSV文件 df = pd.read_csv(file_path, encoding=encoding) # 获取基本信息 info = { "文件名": os.path.basename(file_path), "行数": len(df), "列数": len(df.columns), "列名": list(df.columns), "数据预览": df.head().to_string() } # 生成描述性统计 stats = df.describe().to_string() # 组合结果 result = ( f"CSV文件解析结果:\n\n" f"基本信息:\n" f"- 文件名: {info['文件名']}\n" f"- 行数: {info['行数']}\n" f"- 列数: {info['列数']}\n" f"- 列名: {', '.join(info['列名'])}\n\n" f"数据预览:\n{info['数据预览']}\n\n" f"描述性统计:\n{stats}" ) return [types.TextContent( type="text", text=result )] except Exception as e: error_details = traceback.format_exc() return [types.TextContent( type="text", text=f"错误: 处理CSV文件时发生错误: {str(e)}\n{error_details}" )]