MCP_3

""" MCP.server 的 Docstring 1.创建 fastmcp 的实例 2.创建函数，添加文档 3.@mcp.tool() 4.运行服务器 """ import os import json import arxiv from typing import List, Dict, Any from mcp.server.fastmcp import FastMCP mcp = FastMCP("research") PAPER_DIR = "papers" @mcp.tool() def get_weather(city: str): """获取指定城市的天气信息。 参数: city (str): 城市名称。 返回: str: 天气信息。 """ return f"{city} 的天气是晴天。18度。" @mcp.tool() def add(a: int, b: int) -> int: """将两个整数相加并返回结果。 参数: a (int): 第1个整数。 b (int): 第2个整数。 返回: int: 两个整数的和。 """ return a + b @mcp.tool() def search_papers(topic: str, max_results: int = 5) -> List[str]: """ 基于话题topic和它的存储信息在arxiv上搜索相关论文。 参数: topic (str): 搜索的论文话题。 max_results (int, optional): 最多返回的论文数量。默认为5。 返回: List[str]: 搜索到的论文的IDs列表。 """ # 使用arxiv查找论文 client = arxiv.Client() # 搜索与查询话题最相关的文章 search = arxiv.Search( query=topic, max_results=max_results, sort_by=arxiv.SortCriterion.Relevance, ) papers = client.results(search) # 为此话题创建目录 path = os.path.join(PAPER_DIR, topic.lower().replace(" ", "_")) os.makedirs(path, exist_ok=True) file_paths = os.path.join(path, "papers_info.json") # 尝试加载现有论文信息 try: with open(file_paths, "r") as f: papers_info = json.load(f) except (FileNotFoundError, json.JSONDecodeError): papers_info = {} # 处理每篇论文并添加到paper_info paper_ids = [] for paper in papers: paper_ids.append(paper.get_short_id()) paper_info = { "title": paper.title, "summary": paper.summary, "authors": [author.name for author in paper.authors], "published": str(paper.published.date()), "pdf_url": paper.pdf_url, } papers_info[paper.get_short_id()] = paper_info # 将更新的paper_info保存到json文件 with open(file_paths, "w") as json_file: json.dump(papers_info, json_file, indent=2) print(f"结果已保存到 {file_paths}") return paper_ids #print(search_papers("machine learning")) @mcp.tool() def extract_info(paper_id: str) -> str: """ 在所有话题目录中搜索特定论文的信息。 参数: paper_id: 要查找的论文ID 返回: 如果找到则返回包含论文信息的JSON字符串，否则返回错误信息 """ for item in os.listdir(PAPER_DIR): item_path = os.path.join(PAPER_DIR, item) if os.path.isdir(item_path): file_path = os.path.join(item_path, "papers_info.json") if os.path.isfile(file_path): try: with open(file_path, "r") as json_file: papers_info = json.load(json_file) if paper_id in papers_info: return json.dumps(papers_info[paper_id], indent=2) except (FileNotFoundError, json.JSONDecodeError) as e: print(f"Error reading {file_path}: {str(e)}") continue return f"There's no saved information related to paper {paper_id}." @mcp.resource("papers://folders") def get_available_folders() -> str: """ 列出papers目录中所有可用的主题文件夹。 此资源提供所有可用主题文件夹的简单列表。 """ folders = [] # 获取所有主题目录 if os.path.exists(PAPER_DIR): for topic_dir in os.listdir(PAPER_DIR): topic_path = os.path.join(PAPER_DIR, topic_dir) if os.path.isdir(topic_path): papers_file = os.path.join(topic_path, "papers_info.json") if os.path.exists(papers_file): folders.append(topic_dir) # 创建一个简单的markdown列表 content = "# Available Topics\n\n" if folders: for folder in folders: content += f"- {folder}'\n" content += f"\nUse @{folder} to access papers in that topic.\n" else: content += "No topics found.\n" return content @mcp.resource("papers://{topic}") def get_topic_papers(topic: str) -> str: """ 获取特定主题的论文详细信息。 参数: topic: 要检索论文的研究主题 """ import os import json topic_dir = topic.lower().replace(" ", "_") papers_file = os.path.join(PAPER_DIR, topic_dir, "papers_info.json") if not os.path.exists(papers_file): return f"# No papers found for topic: {topic}\n\nTry searching for papers on this topic first." try: with open(papers_file, 'r') as f: papers_data = json.load(f) # Create markdown content with paper details content = f"# Papers on {topic.replace('_', ' ').title()}\n\n" content += f"Total papers: {len(papers_data)}\n\n" for paper_id, paper_info in papers_data.items(): content += f"## {paper_info['title']}\n" content += f"- **Paper ID**: {paper_id}\n" content += f"- **Authors**: {', '.join(paper_info['authors'])}\n" content += f"- **Published**: {paper_info['published']}\n" content += f"- **PDF URL**: [{paper_info['pdf_url']}]({paper_info['pdf_url']})\n\n" content += f"### Summary\n{paper_info['summary'][:500]}...\n\n" content += "---\n\n" return content except json.JSONDecodeError: return f"# Error reading papers data for {topic}\n\nThe papers data file is corrupted." except FileNotFoundError: return f"# Error: Papers data file not found for {topic}\n\nFile path: {papers_file}" @mcp.prompt() def generate_search_prompt(topic: str, num_papers: int = 5) -> str: """生成一个提示词，用于让Claude查找和讨论特定主题的学术论文。""" return f"""Search for {num_papers} academic papers about '{topic}' using the search_papers tool. Follow these instructions: 1. First, search for papers using search_papers(topic='{topic}', max_results={num_papers}) 2. For each paper found, extract and organize the following information: - Paper title - Authors - Publication date - Brief summary of the key findings - Main contributions or innovations - Methodologies used - Relevance to the topic '{topic}' 3. Provide a comprehensive summary that includes: - Overview of the current state of research in '{topic}' - Common themes and trends across the papers - Key research gaps or areas for future investigation - Most impactful or influential papers in this area 4. Organize your findings in a clear, structured format with headings and bullet points for easy readability. Please present both detailed information about each paper and a high-level synthesis of the research landscape in {topic}.""" if __name__ == "__main__": mcp.run(transport='stdio')