summarize_content

Summarize any content to a specified length ratio (default 20%) for concise understanding. Ideal for condensing information efficiently using controlled compression settings.

Instructions

将任意内容总结为指定比例的长度（默认20%）

Args:
    content: 要总结的内容
    target_ratio: 目标压缩比例，0.1-1.0之间

Returns:
    总结后的内容

Input Schema

TableJSON Schema

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

Implementation Reference

src/fullscope_mcp_server/server.py:340-366 (handler)

The primary MCP tool handler for 'summarize_content'. It is registered via @mcp.tool() decorator, includes input validation (schema-like), logging via ctx, and delegates to the helper summarizer.

@mcp.tool()
async def summarize_content(content: str, ctx: Context, target_ratio: float = 0.2) -> str:
    """
    将任意内容总结为指定比例的长度（默认20%）
    
    Args:
        content: 要总结的内容
        target_ratio: 目标压缩比例，0.1-1.0之间
    
    Returns:
        总结后的内容
    """
    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"开始总结内容，目标比例: {target_ratio*100}%")
        
        summary = await summarizer.summarize_content(content, target_ratio)
        
        ctx.info("内容总结完成")
        return summary
        
    except Exception as e:
        logger.error(f"内容总结失败: {e}")
        return f"内容总结失败: {str(e)}"

src/fullscope_mcp_server/server.py:57-107 (helper)

The core helper function in ContentSummarizer class that performs the actual summarization using OpenAI/MiniMax API. Called by the tool handler and other tools.

    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