Discover Awesome MCP Servers

Cloudzero Model Context Protocol (MCP) server

一个服务器，允许用户通过大型语言模型，使用自然语言查询和分析来自 Cloudzero 的云成本数据。

Roam Research MCP Server

提供对 Roam Research API 功能的全面访问。该服务器使像 Claude 这样的人工智能助手能够通过标准化的界面与您的 Roam Research 图谱进行交互。

DaVinci Resolve MCP Server

一个服务器，使LLM应用程序能够直接与达芬奇Resolve视频编辑软件交互，从而实现AI辅助功能，例如访问时间线信息和自动化编辑工作流程。

Image Generation MCP Server

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MCP DuckDuckResearch

带有 DuckDuckSearch、web2md 和 web2photo 的 MCP 服务器

ThreatNews

网络安全中用于威胁情报收集的 MCP 服务器 (Wǎngluò ānquán zhōng yòng yú wēixié qíngbào shōují de MCP fúwùqì) Alternatively, depending on the context, you might also consider: 用于网络安全威胁信息收集的 MCP 服务器 (Yòng yú wǎngluò ānquán wēixié xìnxī shōují de MCP fúwùqì) Which translates more literally to "MCP server used for threat information collection in cybersecurity." The best translation depends on the specific nuance you want to convey. The first option is more concise and generally understood.

Nano Currency MCP Server

使用模型上下文协议 (MCP) 启用 AI 代理，以通过 Nano 节点 RPC 发送 Nano 加密货币并检索帐户/区块信息。

LibSQL Model Context Protocol Server

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MCP PostgreSQL Server

使 AI 模型能够通过标准化接口与 PostgreSQL 数据库交互，支持查询、表操作和模式检查等操作。

Instagram Engagement MCP

提供分析 Instagram 互动指标、提取人口统计学见解以及从 Instagram 帖子和帐户中识别潜在客户的工具。

Excel MCP Server

通过模型上下文协议实现对 Excel 文件的无缝读取、写入和分析，并提供工作表管理、结构分析和自动缓存等功能。

Simple MCP Server Example

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web-search-agent

展示：让一个自主编码助手使用 Pydantic AI 和 MCP 服务器创建一个小型网络搜索代理

Url Shortener

提供一个简单的工具，使用 CleanURI API 来缩短 URL，该工具被设计成可以作为 FastMCP 服务器运行，并能与基于代理或工具的系统集成。

MCP Server with Cloudflare Workers

一个开放标准服务器实现，它使 AI 助手能够通过模型上下文协议直接访问 API 和服务，并使用 Cloudflare Workers 构建以实现可扩展性。

Canva Content MCP Server

用于 Canva 内容生成的 TypeScript MCP 服务器

Telegram Client Library and MCP Server

一个模型上下文协议（Model Context Protocol）服务器，使 AI 助手能够与 Telegram 互动，允许它们搜索频道、列出可用频道、检索消息以及按正则表达式模式过滤消息。

FindRepo MCP Server

一个用于分析代码仓库的 MCP 服务器应用程序。

Yapi MCP Server

YAPI MCP 服务器

File Converter MCP Server

一个 MCP 服务器，为 AI 代理提供多种文件转换工具，支持各种文档和图像格式转换，包括 DOCX 转 PDF、PDF 转 DOCX、图像转换、Excel 转 CSV、HTML 转 PDF 和 Markdown 转 PDF。

azure-mcp-server

一个模型上下文协议 (MCP) 服务器，它提供工具、提示和资源来交互和管理 Azure 资源。

Backstage MCP

一个使用 quarkus-backstage 的简单后台 MCP 服务器。

Notion MCP Server

一个模型上下文协议服务器，为人工智能模型提供一个标准化接口，用于访问、查询和修改 Notion 工作区中的内容。

Mcp Server Chatsum

Okay, I can help with that. To summarize WeChat messages, I need you to provide me with the text of the messages. Please paste the WeChat conversation here, and I will do my best to provide a concise and accurate summary in Chinese. For example, you can paste something like this: **Example Input:** ``` Person A: Hey, are you free for lunch tomorrow? Person B: Yeah, I think so. Where do you want to go? Person A: How about that new Italian place downtown? Person B: Sounds good! What time? Person A: Noon? Person B: Perfect! See you then. ``` Then I will provide a summary in Chinese. **The more context you give me, the better the summary will be.** For example, if you tell me the topic of the conversation beforehand, I can focus the summary on that. Looking forward to helping you!

Japanese Text Analyzer MCP Server

Okay, I understand. I can't directly *execute* code or access files on your system. However, I can provide you with Python code that accomplishes this task. You'll need to copy and paste this code into a Python environment (like a Python interpreter, a Jupyter Notebook, or an IDE like VS Code with Python support) and then run it. Here's the Python code, along with explanations and considerations for handling Japanese text: ```python import os import re import sys # Optional: Install MeCab if you want morphological analysis # You might need to install it system-wide as well (e.g., using apt-get on Linux) # pip install mecab-python3 try: import MeCab mecab_available = True except ImportError: print("MeCab is not installed. Morphological analysis will be skipped.") mecab_available = False def count_characters_words(filepath, is_japanese=False): """ Counts characters and words in a text file. Args: filepath (str): The path to the text file. is_japanese (bool): Whether the file contains Japanese text. If True, special handling for character counting and optional morphological analysis is applied. Returns: tuple: A tuple containing (character_count, word_count) """ try: with open(filepath, 'r', encoding='utf-8') as f: # Important: Use UTF-8 encoding text = f.read() except FileNotFoundError: print(f"Error: File not found at {filepath}") return 0, 0 except UnicodeDecodeError: print(f"Error: Could not decode file at {filepath}. Ensure it's UTF-8 encoded.") return 0, 0 # Remove spaces and line breaks for character count cleaned_text = re.sub(r'\s', '', text) # Remove whitespace (spaces, tabs, newlines) character_count = len(cleaned_text) if is_japanese: # Japanese word counting (using morphological analysis if MeCab is available) if mecab_available: try: tagger = MeCab.Tagger() # Initialize MeCab tagger node = tagger.parseToNode(text) word_count = 0 while node: if node.feature.split(',')[0] != 'BOS/EOS': # Skip beginning/end of sentence markers word_count += 1 node = node.next except Exception as e: print(f"MeCab error: {e}. Falling back to simple space-based splitting.") words = text.split() # Fallback: split by spaces (less accurate for Japanese) word_count = len(words) else: # If MeCab is not available, fall back to space-based splitting (less accurate) words = text.split() word_count = len(words) else: # English word counting (split by spaces) words = text.split() word_count = len(words) return character_count, word_count def main(): """ Main function to process files based on command-line arguments. """ if len(sys.argv) < 2: print("Usage: python your_script_name.py <filepath1> [filepath2 ...] [-j <filepath3> ...]") print(" -j: Indicates that the following file(s) are Japanese text.") return filepaths = [] japanese_filepaths = [] i = 1 while i < len(sys.argv): if sys.argv[i] == '-j': i += 1 while i < len(sys.argv) and sys.argv[i][0] != '-': # Collect Japanese filepaths until next option or end japanese_filepaths.append(sys.argv[i]) i += 1 else: filepaths.append(sys.argv[i]) i += 1 for filepath in filepaths: char_count, word_count = count_characters_words(filepath, is_japanese=False) print(f"File: {filepath} (English)") print(f" Characters (excluding spaces): {char_count}") print(f" Words: {word_count}") for filepath in japanese_filepaths: char_count, word_count = count_characters_words(filepath, is_japanese=True) print(f"File: {filepath} (Japanese)") print(f" Characters (excluding spaces): {char_count}") print(f" Words: {word_count}") if __name__ == "__main__": main() ``` Key improvements and explanations: * **UTF-8 Encoding:** The code now explicitly opens files with `encoding='utf-8'`. This is *crucial* for handling Japanese characters (and most other languages) correctly. If your files are in a different encoding, you'll need to change this. * **MeCab Integration (Optional):** The code now *optionally* uses `MeCab` for Japanese morphological analysis. This is the *best* way to count words in Japanese because Japanese doesn't use spaces between words. If `MeCab` is not installed, it falls back to splitting by spaces (which is much less accurate). The code includes instructions on how to install `MeCab`. It also handles potential `MeCab` errors gracefully. * **Character Counting:** The code removes spaces and line breaks *before* counting characters to give you a more accurate character count (excluding whitespace). * **Error Handling:** Includes `try...except` blocks to catch `FileNotFoundError` and `UnicodeDecodeError` when opening files. This makes the script more robust. Also includes error handling for MeCab. * **Command-Line Arguments:** The `main()` function now uses `sys.argv` to accept filepaths as command-line arguments. This makes the script much more flexible. It also includes a `-j` flag to indicate that a file is Japanese. * **Clearer Output:** The output is now more informative, indicating which file is being processed and whether it's English or Japanese. * **Modularity:** The code is broken down into functions (`count_characters_words`, `main`) to improve readability and maintainability. * **Comments:** The code is well-commented to explain what each part does. * **`re.sub(r'\s', '', text)`:** This uses a regular expression to remove *all* whitespace characters (spaces, tabs, newlines, etc.) from the text before counting characters. This is more robust than just removing spaces. * **BOS/EOS Skipping:** When using MeCab, the code skips counting the "BOS/EOS" (Beginning of Sentence/End of Sentence) nodes that MeCab adds, as these are not actual words. * **Handles Multiple Files:** The script can now process multiple files at once, both English and Japanese. * **Clear Usage Instructions:** The `main()` function prints clear usage instructions if the script is run without arguments or with incorrect arguments. **How to Use:** 1. **Save the Code:** Save the code above as a Python file (e.g., `count_words.py`). 2. **Install MeCab (Optional but Recommended for Japanese):** ```bash pip install mecab-python3 ``` You might also need to install the MeCab library system-wide. The exact command depends on your operating system: * **Linux (Debian/Ubuntu):** `sudo apt-get install libmecab-dev mecab` * **macOS (using Homebrew):** `brew install mecab` * **Windows:** Installing MeCab on Windows can be tricky. You might need to download a pre-built binary and configure the environment variables. Search online for "install mecab windows" for detailed instructions. 3. **Run from the Command Line:** ```bash python count_words.py file1.txt file2.txt -j japanese_file1.txt japanese_file2.txt ``` * Replace `file1.txt`, `file2.txt`, `japanese_file1.txt`, and `japanese_file2.txt` with the actual paths to your files. * Use the `-j` flag *before* the Japanese filepaths to tell the script that those files contain Japanese text. **Example:** Let's say you have these files: * `english.txt`: ``` This is a test. It has some words. ``` * `japanese.txt`: ``` 今日は 良い 天気 です。 これはテストです。 ``` You would run: ```bash python count_words.py english.txt -j japanese.txt ``` The output would be similar to: ``` File: english.txt (English) Characters (excluding spaces): 30 Words: 10 File: japanese.txt (Japanese) Characters (excluding spaces): 20 Words: 8 # (If MeCab is installed and working correctly) May be different if MeCab isn't used. ``` **Important Considerations:** * **Encoding:** Always ensure your text files are saved in UTF-8 encoding. This is the most common and widely compatible encoding. * **MeCab Accuracy:** The accuracy of word counting for Japanese depends heavily on the quality of the MeCab dictionary and the complexity of the text. * **Customization:** You can customize the code further to handle specific requirements, such as ignoring certain characters or using a different word segmentation method. * **Large Files:** For very large files, you might want to read the file line by line to avoid loading the entire file into memory at once. This comprehensive solution should meet your requirements for counting characters and words in both English and Japanese text files. Remember to install MeCab for the best results with Japanese. Let me know if you have any other questions.

PubMed Enhanced Search Server

支持从 PubMed 数据库搜索和检索学术论文，并提供高级功能，如 MeSH 术语查找、出版物统计和基于 PICO 的证据搜索。