FullScope-MCP

Instructions

读取txt等格式的文本文件并总结内容（限制2k字符）

Args:
    filepath: 文本文件路径
    target_ratio: 目标压缩比例，0.1-1.0之间

Returns:
    文件内容总结

Implementation Reference

• src/fullscope_mcp_server/server.py:401-432 (handler)

  The main execution handler for the read_and_summarize_text_file tool, registered with @mcp.tool(). Validates input, reads the file using file_processor.read_text_file(), summarizes using summarizer.summarize_content(), and returns formatted result.

  @mcp.tool()
  async def read_and_summarize_text_file(filepath: str, ctx: Context, target_ratio: float = 0.2) -> str:
      """
      读取txt等格式的文本文件并总结内容（限制2k字符）
      
      Args:
          filepath: 文本文件路径
          target_ratio: 目标压缩比例，0.1-1.0之间
      
      Returns:
          文件内容总结
      """
      try:
          # 验证参数
          if not 0.1 <= target_ratio <= 1.0:
              return "错误: target_ratio 必须在 0.1 到 1.0 之间"
          
          ctx.info(f"开始读取文本文件: {filepath}")
          
          # 读取文件
          content = file_processor.read_text_file(filepath)
          
          # 总结内容
          summary = await summarizer.summarize_content(content, target_ratio)
          
          ctx.info("文本文件读取和总结完成")
          return f"文件: {filepath}\n\n总结:\n{summary}"
          
      except Exception as e:
          logger.error(f"文本文件总结失败: {e}")
          return f"文本文件总结失败: {str(e)}"

• src/fullscope_mcp_server/server.py:188-218 (helper)

  FileProcessor class with static method read_text_file used by the tool to read the content of the specified text file.

  class FileProcessor:
      """文件处理器"""
      
      @staticmethod
      def read_text_file(filepath: str) -> str:
          """读取文本文件"""
          try:
              with open(filepath, 'r', encoding='utf-8') as f:
                  content = f.read()
              
              return content
          except Exception as e:
              logger.error(f"读取文本文件失败 {filepath}: {e}")
              raise Exception(f"无法读取文件: {str(e)}")
      
      @staticmethod
      def read_pdf_file(filepath: str) -> str:
          """读取PDF文件"""
          try:
              content = ""
              with open(filepath, 'rb') as f:
                  pdf_reader = PyPDF2.PdfReader(f)
                  
                  for page in pdf_reader.pages:
                      content += page.extract_text() + "\n"
              
              return content.strip()
          except Exception as e:
              logger.error(f"读取PDF文件失败 {filepath}: {e}")
              raise Exception(f"无法读取PDF文件: {str(e)}")

• src/fullscope_mcp_server/server.py:45-107 (helper)

  ContentSummarizer class providing the summarize_content method, which uses an OpenAI-compatible client to generate summaries based on target_ratio. This is the core summarization logic called by the tool.

  class ContentSummarizer:
      """内容总结器，使用MiniMax API"""
      
      def __init__(self):
          if not OPENAI_API_KEY:
              raise ValueError("需要设置OPENAI_API_KEY环境变量")
          
          self.client = OpenAI(
              api_key=OPENAI_API_KEY,
              base_url=OPENAI_BASE_URL
          )
      
      async def summarize_content(self, content: str, target_ratio: float = 0.2, 
                                custom_prompt: str = None) -> str:
          """
          使用大模型总结内容
          
          Args:
              content: 要总结的内容
              target_ratio: 目标压缩比例 (默认20%)
              custom_prompt: 自定义总结提示词
          
          Returns:
              总结后的内容
          """
          try:
              # 检查内容长度，避免超出限制
              if len(content) > MAX_INPUT_TOKENS * 3:  # 粗略估算token
                  content = content[:MAX_INPUT_TOKENS * 3]
                  logger.warning("内容过长，已截断")
              
              # 构建总结提示词
              if custom_prompt:
                  prompt = custom_prompt
              else:
                  target_length = min(max(int(len(content) * target_ratio), 100), 1000)
  
                  prompt = f"""请将以下内容总结为约{target_length}字的精炼版本，保留核心信息和关键要点：
  
  {content}
  
  总结要求：
  1. 保持原文的主要观点和逻辑结构
  2. 去除冗余和次要信息
  3. 使用简洁明了的语言
  4. 确保信息的准确性和完整性"""
  
              response = self.client.chat.completions.create(
                  model=OPENAI_MODEL,
                  messages=[
                      {"role": "system", "content": "你是一个专业的内容总结专家，擅长将长文本压缩为精炼的摘要。"},
                      {"role": "user", "content": prompt}
                  ],
                  max_tokens=MAX_OUTPUT_TOKENS,
                  temperature=0.1
              )
              
              return response.choices[0].message.content.strip()
              
          except Exception as e:
              logger.error(f"内容总结失败: {e}")
              return f"总结失败: {str(e)}"