summarize_webpage
Extract and condense webpage content to a specified length, providing concise summaries for quick understanding. Input a URL and set target compression ratio for tailored results.
Instructions
抓取网页内容并总结为指定比例的长度（默认20%）
Args:
url: 要抓取和总结的网页URL
target_ratio: 目标压缩比例，0.1-1.0之间
Returns:
网页内容总结
Input Schema
TableJSON Schema
| Name | Required | Description | Default |
|---|---|---|---|
| target_ratio | No | ||
| url | Yes |
Implementation Reference
- The primary handler function for the 'summarize_webpage' tool. It validates parameters, scrapes the webpage using the WebScraper instance, summarizes the title and content using the ContentSummarizer instance, and returns a formatted summary with title and URL.@mcp.tool() async def summarize_webpage(url: str, ctx: Context, target_ratio: float = 0.2) -> str: """ 抓取网页内容并总结为指定比例的长度（默认20%） Args: url: 要抓取和总结的网页URL target_ratio: 目标压缩比例，0.1-1.0之间 Returns: 网页内容总结 """ 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"开始抓取并总结网页: {url}") # 先抓取网页 title, content = await scraper.scrape_url(url) # 然后总结 full_content = f"网页标题: {title}\n\n{content}" summary = await summarizer.summarize_content(full_content, target_ratio) ctx.info("网页抓取和总结完成") return f"网页: {url}\n标题: {title}\n\n总结:\n{summary}" except Exception as e: logger.error(f"网页总结失败: {e}") return f"网页总结失败: {str(e)}"
- src/fullscope_mcp_server/server.py:368-368 (registration)The @mcp.tool() decorator registers the summarize_webpage function as an MCP tool.@mcp.tool()
- Function signature and docstring defining the input parameters (url: str, ctx: Context, target_ratio: float=0.2) and output (str), serving as the tool schema.async def summarize_webpage(url: str, ctx: Context, target_ratio: float = 0.2) -> str: """ 抓取网页内容并总结为指定比例的长度（默认20%） Args: url: 要抓取和总结的网页URL target_ratio: 目标压缩比例，0.1-1.0之间 Returns: 网页内容总结 """
- WebScraper.scrape_url: Supporting function that fetches and parses the webpage HTML using httpx and BeautifulSoup, extracts title and cleans the text content.async def scrape_url(self, url: str) -> tuple[str, str]: """ 抓取网页内容 Args: url: 目标URL Returns: (title, content): 网页标题和清理后的文本内容 """ try: response = await self.session.get(url) response.raise_for_status() # 使用BeautifulSoup解析HTML soup = BeautifulSoup(response.text, 'html.parser') # 获取标题 title = soup.find('title') title = title.get_text().strip() if title else "无标题" # 移除script和style标签 for script in soup(["script", "style"]): script.decompose() # 提取主要内容 content = soup.get_text() # 清理文本 lines = (line.strip() for line in content.splitlines()) chunks = (phrase.strip() for line in lines for phrase in line.split(" ")) content = ' '.join(chunk for chunk in chunks if chunk) return title, content except Exception as e: logger.error(f"网页抓取失败 {url}: {e}") raise Exception(f"无法抓取网页: {str(e)}")
- ContentSummarizer.summarize_content: Supporting function that uses OpenAI-compatible API (MiniMax) to summarize the provided content based on target ratio, with custom prompt option.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)}"