Discover Awesome MCP Servers

mcp-excalidraw

LLM이 구조화된 API를 통해 Excalidraw 다이어그램을 생성, 수정 및 조작할 수 있도록 하는 모델 컨텍스트 프로토콜 서버입니다.

mcp-server-cli

Model Context Protocol server to run shell scripts or commands

G-Search MCP

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

mcp-osint OSINT Server

MCP 서버는 일반적인 네트워크 정찰 도구를 활용하여 다양한 OSINT 작업을 수행합니다.

AgentCraft MCP Server

AgentCraft 프레임워크와 통합되어 AI 에이전트 간의 안전한 통신 및 데이터 교환을 지원하며, 사전 제작된 AI 에이전트와 사용자 정의 엔터프라이즈 AI 에이전트를 모두 지원합니다.

ThemeParks.wiki API MCP Server

ThemeParks.wiki API MCP 서버

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.

MCP Etherscan Server

거울

MCP SSH Server for Windsurf

윈드서프 통합을 위한 MCP SSH 서버

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.

Backstage MCP

A simple backstage mcp server using quarkus-backstage

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.

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.

reddit-mcp

MCP server for reddit.

MCP Image Generation Server

MCP (Model Context Protocol) 서버 도구의 Go 구현

Strava MCP Server

사용자가 구조화된 API 인터페이스를 통해 Strava 피트니스 데이터 (사용자 활동, 활동 상세 정보, 구간, 리더보드 포함)에 접근할 수 있도록 하는 모델 컨텍스트 프로토콜 서버.

Selector Mcp Server

Model Context Protocol (MCP) 서버는 스트리밍을 지원하는 서버와 Docker 기반 클라이언트를 통해 Selector AI와 실시간으로 상호 작용하는 AI 채팅을 가능하게 합니다. 이 서버와 클라이언트는 stdin/stdout을 통해 통신합니다.

For the GitHub MCP

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

Data.gov MCP Server

거울

Model Context Protocol (MCP)

The Model Context Protocol is an open standard that enables developers to build secure, two-way connections between their data sources and AI-powered tools. The architecture is straightforward: developers can either expose their data through MCP servers or build AI applications (MCP clients) that connect to these servers.

NN-GitHubTestRepo

MCP 서버 데모에서 생성됨

➡️ browser-use mcp server

AI 어시스턴트가 자연어 명령을 통해 웹 브라우저를 제어하고, SSE 전송을 통해 웹사이트를 탐색하고 정보를 추출할 수 있도록 하는 MCP 서버입니다.