Discover Awesome MCP Servers

MCP SSH Server for Windsurf

윈드서프 통합을 위한 MCP SSH 서버

dbx-mcp-server

AI 애플리케이션이 Dropbox와 상호 작용할 수 있도록 지원하는 모델 컨텍스트 프로토콜 서버입니다. Dropbox API를 통해 파일 작업, 메타데이터 검색, 검색 및 계정 관리 도구를 제공합니다.

Backstage MCP

A simple backstage mcp server using quarkus-backstage

MCP Etherscan Server

거울

mcp-server-cli

Model Context Protocol server to run shell scripts or commands

reddit-mcp

MCP server for reddit.

G-Search MCP

여러 키워드를 동시에 사용하여 병렬 Google 검색을 가능하게 하고, CAPTCHA를 처리하며 사용자 검색 패턴을 시뮬레이션하는 구조화된 결과를 제공하는 강력한 MCP 서버.

Notion MCP Server

AI 모델이 Notion 워크스페이스의 콘텐츠에 접근, 쿼리, 수정할 수 있도록 표준화된 인터페이스를 제공하는 모델 컨텍스트 프로토콜 서버.

filesystem

Claude 또는 다른 AI 어시스턴트에게 파일 시스템 접근 및 관리 기능을 제공하여 AI 기능을 확장하는 모델 컨텍스트 프로토콜 서버입니다.

Mcp Server Chatsum

Please provide me with the WeChat messages you want me to summarize. I need the text of the messages to be able to summarize them for 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 a Python script that accomplishes the task you described. You can then copy and paste this script into a Python environment on your computer and run it. Here's the Python script with detailed comments explaining each part: ```python import re import os import argparse # For command-line arguments import subprocess # For calling MeCab def count_characters_and_words(filepath, language="english"): """ Counts characters and words in a text file, handling Japanese differently. Args: filepath (str): The path to the text file. language (str): "english" or "japanese". Determines the counting method. Returns: tuple: (character_count, word_count) """ try: with open(filepath, 'r', encoding='utf-8') as f: 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 {filepath}. Ensure it's UTF-8 encoded.") return 0, 0 if language == "english": # Remove spaces and line breaks for character count character_count = len(re.sub(r'\s', '', text)) # Remove whitespace words = text.split() # Split by whitespace word_count = len(words) elif language == "japanese": # Use MeCab for morphological analysis try: mecab_process = subprocess.Popen(['mecab'], stdin=subprocess.PIPE, stdout=subprocess.PIPE, stderr=subprocess.PIPE, text=True) mecab_output, mecab_error = mecab_process.communicate(text) if mecab_error: print(f"MeCab Error: {mecab_error}") return 0, 0 # Count characters (excluding spaces and line breaks) character_count = len(re.sub(r'\s', '', text)) # Count words based on MeCab output (count nouns, verbs, adjectives, etc.) word_count = 0 for line in mecab_output.splitlines(): if line == "EOS": # End of Sentence marker break parts = line.split('\t') if len(parts) > 1: pos = parts[1].split(',')[0] # Part of Speech # Adjust the POS tags to count based on your needs. This is a starting point. if pos in ['名詞', '動詞', '形容詞', '副詞', '連体詞', '接続詞', '感動詞']: # Noun, Verb, Adjective, Adverb, Adnominal Adjective, Conjunction, Interjection word_count += 1 except FileNotFoundError: print("Error: MeCab is not installed or not in your PATH.") print("Please install MeCab and ensure it's accessible from the command line.") return 0, 0 except Exception as e: print(f"An unexpected error occurred during MeCab processing: {e}") return 0, 0 else: print("Error: Invalid language specified. Choose 'english' or 'japanese'.") return 0, 0 return character_count, word_count def main(): parser = argparse.ArgumentParser(description="Count characters and words in a text file.") parser.add_argument("filepath", help="Path to the text file") parser.add_argument("-l", "--language", default="english", choices=["english", "japanese"], help="Language of the text (english or japanese)") args = parser.parse_args() filepath = args.filepath language = args.language char_count, word_count = count_characters_and_words(filepath, language) if char_count is not None and word_count is not None: # Check for errors print(f"File: {filepath}") print(f"Language: {language}") print(f"Character Count (excluding spaces): {char_count}") print(f"Word Count: {word_count}") if __name__ == "__main__": main() ``` Key improvements and explanations: * **Error Handling:** Includes `try...except` blocks to handle `FileNotFoundError`, `UnicodeDecodeError`, and MeCab-related errors. This makes the script much more robust. It also checks for `None` return values from `count_characters_and_words` to prevent errors if the function fails. * **MeCab Integration:** Uses `subprocess.Popen` to call MeCab from Python. This is the standard way to interact with external command-line tools. It captures both standard output and standard error from MeCab, allowing for better error reporting. The `text=True` argument ensures that the input and output are handled as text strings. * **MeCab Output Parsing:** Parses the MeCab output to count words based on Part-of-Speech (POS) tags. The script now extracts the POS tag and checks if it's a noun, verb, adjective, etc. **Important:** You might need to adjust the list of POS tags to count based on your specific needs and the conventions used by your MeCab dictionary. The provided list is a common starting point. * **Character Counting (Japanese):** The character count for Japanese also excludes spaces and line breaks. * **UTF-8 Encoding:** Opens the file with `encoding='utf-8'` to handle Unicode characters correctly. This is crucial for Japanese text. * **Command-Line Arguments:** Uses `argparse` to allow you to specify the filepath and language from the command line. This makes the script much more flexible. * **Clearer Comments:** More detailed comments explaining each part of the code. * **`if __name__ == "__main__":` block:** Ensures that the `main()` function is only called when the script is run directly (not when it's imported as a module). * **Returns 0, 0 on Error:** The `count_characters_and_words` function now returns `0, 0` if an error occurs, making it easier to handle errors in the `main` function. * **Checks for MeCab Installation:** The script now checks if MeCab is installed and in the system's PATH. If not, it prints an informative error message. * **More Robust MeCab Error Handling:** The script now checks for errors returned by MeCab (in `mecab_error`) and prints them to the console. * **Handles Unexpected MeCab Errors:** Includes a general `except Exception as e:` block to catch any unexpected errors during MeCab processing. **How to Use:** 1. **Save the script:** Save the code above as a Python file (e.g., `count_text.py`). 2. **Install MeCab (if you haven't already):** * **Linux (Debian/Ubuntu):** `sudo apt-get install mecab libmecab-dev mecab-ipadic-utf8` * **macOS:** `brew install mecab` (if you have Homebrew) * **Windows:** Installation on Windows is more complex. You'll need to download the MeCab binaries and dictionary, and configure your system's PATH environment variable. Refer to the MeCab documentation for Windows installation instructions. A common approach is to use the installer from [https://taku910.github.io/mecab/](https://taku910.github.io/mecab/). Make sure to add the MeCab bin directory to your PATH. 3. **Run the script from the command line:** ```bash python count_text.py <filepath> -l <language> ``` * Replace `<filepath>` with the actual path to your text file. * Replace `<language>` with either `english` or `japanese`. If you omit `-l <language>`, it defaults to `english`. Example: ```bash python count_text.py my_english_text.txt python count_text.py japanese_novel.txt -l japanese ``` **Example Japanese Text File (japanese_novel.txt):** ``` これは日本語の小説です。 今日はいい天気ですね。 猫が好きです。 ``` **Important Considerations for Japanese:** * **MeCab Dictionary:** The accuracy of word counting for Japanese depends heavily on the MeCab dictionary you are using. The default dictionary (`mecab-ipadic-utf8` on Linux) is a good starting point, but you might need to use a different dictionary for specialized vocabulary. * **Part-of-Speech Tagging:** The script currently counts words based on a specific set of POS tags (nouns, verbs, adjectives, etc.). You might need to adjust this list based on your specific needs and the conventions used by your MeCab dictionary. Inspect the MeCab output for your text to see the POS tags that are being assigned. You can do this by running `mecab` on your text file directly from the command line: `mecab japanese_novel.txt`. * **Word Definition:** What constitutes a "word" in Japanese is a complex linguistic question. MeCab provides a morphological analysis, but you need to decide how to interpret that analysis for your word count. The current script provides a reasonable starting point, but you might need to refine it based on your specific requirements. This improved script provides a more robust and accurate solution for counting characters and words in both English and Japanese text files. Remember to install MeCab and adjust the POS tag filtering as needed for Japanese.

PubMed Enhanced Search Server

MeSH 용어 검색, 출판 통계, PICO 기반 증거 검색과 같은 고급 기능을 통해 PubMed 데이터베이스에서 학술 논문을 검색하고 검색할 수 있습니다.

S3 MCP Server

Claude와 같은 대규모 언어 모델이 AWS S3 스토리지와 상호 작용할 수 있도록 지원하는 Amazon S3 모델 컨텍스트 프로토콜 서버입니다. 버킷 목록 보기, 객체 목록 보기, 객체 내용 검색과 같은 도구를 제공합니다.

perplexity-server MCP Server

Perplexity MCP Server for Cline

openpyxl_mcp_server

OpenPyXl Python 라이브러리를 얇게 감싼 래퍼로, 엑셀 파일 작업을 모델 컨텍스트 프로토콜(MCP) 서버로 노출하여 Claude 및 기타 MCP 클라이언트가 엑셀 파일에서 데이터를 가져와 분석할 수 있도록 합니다.

Legion MCP (Model Context Protocol) Server

Model Context Protocol (MCP) Python SDK와 통합된 Query Runner를 사용하여 데이터베이스의 데이터에 접근하고 쿼리할 수 있도록 돕는 서버입니다. 지원하는 데이터베이스는 다음과 같습니다. * PostgreSQL * Redshift * MySQL * Microsoft SQL Server * Google APIs * Amazon Web Services (boto3를 통해) * CockroachDB * SQLite

OpenAPI MCP Server

이 도구는 OpenAPI v3.1 사양을 가진 모든 API의 프록시 역할을 하는 MCP(Model Context Protocol) 서버를 생성합니다. 이를 통해 Claude Desktop을 사용하여 로컬 및 원격 서버 API와 쉽게 상호 작용할 수 있습니다.

EVM MCP Server

AI 에이전트가 통합 인터페이스를 통해 여러 EVM 호환 블록체인 네트워크와 상호 작용할 수 있도록 지원하며, ENS 확인, 토큰 작업, 스마트 컨트랙트 상호 작용을 지원하는 포괄적인 서버.

OneSignal MCP Server

OneSignal REST API를 래핑하여 여러 OneSignal 애플리케이션에서 푸시 알림, 이메일, SMS, 사용자 장치 및 세그먼트를 관리할 수 있도록 하는 모델 컨텍스트 프로토콜 서버입니다.

Linear MCP Server

AI 어시스턴트가 Model Context Protocol (MCP) 표준을 통해 Linear 티켓 데이터에 접근하고 검색할 수 있도록 하는 서버입니다. 현재는 사용자의 할 일 티켓을 가져오는 데 중점을 두고 있습니다.

Dynamic Shell Server

A Model Context Protocol (MCP) server that enables secure execution of shell commands with a dynamic approval system. This server allows running arbitrary commands while maintaining security through user approval and audit logging.

quickchart-server MCP Server

QuickChart.io를 사용하여 다양한 차트 유형과 Chart.js 설정을 지원하며 사용자 정의 가능한 데이터 시각화를 생성하는 MCP 서버입니다.

For the GitHub MCP

A LangGraph incorporating the Selector MCP Server and other MCP Servers as an example of a modern solution

Kafka MCP Server

AI 모델이 표준화된 인터페이스를 통해 Apache Kafka 토픽에서 메시지를 게시하고 소비할 수 있도록 하여, Kafka 메시징을 LLM 및 에이전트 애플리케이션과 쉽게 통합할 수 있게 합니다.