read_and_summarize_pdf_file

Extract and condense PDF content into a concise summary by specifying the file path and desired compression ratio. Ideal for quickly accessing key information from lengthy documents.

Instructions

读取PDF文件并总结内容（限制2k字符）

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

Returns:
    PDF内容总结

Input Schema

TableJSON Schema

Name	Required	Description	Default
`filepath`	Yes
`target_ratio`	No

Implementation Reference

src/fullscope_mcp_server/server.py:433-463 (handler)

The main execution function for the tool, decorated with @mcp.tool() which also handles registration. Validates input, reads PDF content via helper, summarizes it, and formats the response.

@mcp.tool()
async def read_and_summarize_pdf_file(filepath: str, ctx: Context, target_ratio: float = 0.2) -> str:
    """
    读取PDF文件并总结内容（限制2k字符）
    
    Args:
        filepath: PDF文件路径
        target_ratio: 目标压缩比例，0.1-1.0之间
    
    Returns:
        PDF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PDF文件: {filepath}")
        
        # 读取PDF
        content = file_processor.read_pdf_file(filepath)
        
        # 总结内容
        summary = await summarizer.summarize_content(content, target_ratio)
        
        ctx.info("PDF文件读取和总结完成")
        return f"PDF文件: {filepath}\n\n总结:\n{summary}"
        
    except Exception as e:
        logger.error(f"PDF文件总结失败: {e}")
        return f"PDF文件总结失败: {str(e)}"

src/fullscope_mcp_server/server.py:203-217 (helper)

Helper method in FileProcessor class to read and extract text from PDF files using PyPDF2.

@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:57-106 (helper)

Core summarization helper in ContentSummarizer class that calls the LLM API to condense content based on target ratio.

    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)}"

FullScope-MCP