Discover Awesome MCP Servers

Algorand MCP Implementation

Server MCP komprehensif untuk interaksi alat (40+) dan aksesibilitas sumber daya (60+) dengan blockchain Algorand, ditambah banyak perintah yang berguna.

Code Summarizer

Memungkinkan alat LLM seperti Claude Desktop dan Cursor AI untuk mengakses dan meringkas file kode melalui server Model Context Protocol, menyediakan akses terstruktur ke konten basis kode tanpa penyalinan manual.

Scast

Mengubah kode menjadi diagram UML dan diagram alur melalui analisis statis, memungkinkan visualisasi struktur kode dan penjelasan fungsionalitas.

DeepView MCP

Server Protokol Konteks Model yang memungkinkan IDE seperti Cursor dan Windsurf untuk menganalisis basis kode yang besar menggunakan jendela konteks Gemini yang luas.

MCP Server for RSS3

Implementasi server MCP yang mengintegrasikan API RSS3, memungkinkan pengguna untuk menanyakan data dari rantai terdesentralisasi, platform media sosial, dan jaringan RSS3 melalui bahasa alami.

IDA Pro MCP Server

Sebuah server Protokol Konteks Model yang memungkinkan asisten AI berinteraksi dengan IDA Pro untuk tugas rekayasa balik dan analisis biner.

memos-mcp-server

A MCP(Model Context Protocol) server for Memos.

datadog-mcp-server

MCP Server for Datadog

vrchat-mcp-osc

Menyediakan jembatan antara asisten AI dan VRChat, memungkinkan kontrol avatar berbasis AI dan interaksi di lingkungan realitas virtual melalui Model Context Protocol.

MCP Server for Milvus

Sebuah server integrasi yang mengimplementasikan Model Context Protocol yang memungkinkan aplikasi LLM berinteraksi dengan fungsionalitas basis data vektor Milvus, memungkinkan pencarian vektor, manajemen koleksi, dan operasi data melalui bahasa alami.

LLMling

Easy MCP (Model Context Protocol) servers and AI agents, defined as YAML.

Jira MCP Server

Server Protokol Konteks Model yang memungkinkan asisten AI seperti Claude untuk berinteraksi dengan Jira, memungkinkan tugas manajemen proyek seperti membuat daftar proyek, mencari masalah, membuat tiket, dan mengelola sprint melalui kueri bahasa alami.

mcp-osint OSINT Server

Server MCP untuk melakukan berbagai tugas OSINT dengan memanfaatkan alat pengintaian jaringan umum.

AgentCraft MCP Server

Terintegrasi dengan kerangka kerja AgentCraft untuk memungkinkan komunikasi dan pertukaran data yang aman antara agen AI, mendukung baik agen AI perusahaan yang sudah jadi maupun yang dibuat khusus.

mcp-excalidraw

A Model Context Protocol server that enables LLMs to create, modify, and manipulate Excalidraw diagrams through a structured API.

@f4ww4z/mcp-mysql-server

Cermin dari

MCP SSE demo

demo of MCP SSE server limitations using the bun runtime

ThemeParks.wiki API MCP Server

API MCP Server ThemeParks.wiki

S3 MCP Server

Sebuah server Protokol Konteks Model Amazon S3 yang memungkinkan Model Bahasa Besar seperti Claude untuk berinteraksi dengan penyimpanan AWS S3, menyediakan alat untuk mendaftar bucket, mendaftar objek, dan mengambil konten objek.

MCP Etherscan Server

Cermin dari

MCP SSH Server for Windsurf

MCP SSH server for Windsurf integration

mcp-server-cli

Model Context Protocol server to run shell scripts or commands

G-Search MCP

Server MCP yang kuat yang memungkinkan pencarian Google paralel dengan banyak kata kunci secara bersamaan, memberikan hasil terstruktur sambil menangani CAPTCHA dan mensimulasikan pola penjelajahan pengguna.

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 import subprocess # For calling MeCab def count_characters_and_words(filepath, language): """ 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 using UTF-8. Try a different encoding.") return 0, 0 if language == "english": # Remove spaces and line breaks for character count characters = re.sub(r'\s', '', text) # Remove whitespace character_count = len(characters) words = text.split() word_count = len(words) elif language == "japanese": # Use MeCab for morphological analysis to get accurate word count try: process = subprocess.Popen(['mecab'], stdin=subprocess.PIPE, stdout=subprocess.PIPE, stderr=subprocess.PIPE, text=True) mecab_output, mecab_error = process.communicate(text) if mecab_error: print(f"MeCab Error: {mecab_error}") return 0, 0 # Count characters (excluding spaces and line breaks) characters = re.sub(r'\s', '', text) character_count = len(characters) # Count words based on MeCab output. Each line is a word. word_count = len(mecab_output.splitlines()) - 1 # Subtract 1 to exclude the empty last line 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 else: print("Error: Invalid language specified. Must be 'english' or 'japanese'.") return 0, 0 return character_count, word_count def main(): """ Main function to handle command-line arguments and call the counting function. """ 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("language", 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 != 0 or word_count != 0: # Only print if there were no errors print(f"File: {filepath}") print(f"Language: {language}") print(f"Character Count (excluding spaces and line breaks): {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` and `UnicodeDecodeError` when opening the file. Also handles potential errors from MeCab. This makes the script much more robust. * **MeCab Integration:** Uses `subprocess.Popen` to call MeCab from Python. This is the standard way to interact with external command-line tools. The `text=True` argument ensures that the input and output are handled as text (Unicode). Critically, it now checks for `mecab_error` and prints it if there is one. * **Character Counting (Japanese):** Removes spaces and line breaks *before* counting characters, as requested. * **Word Counting (Japanese):** Parses the output of MeCab to count words. MeCab outputs each word on a new line, so we split the output by lines and count the lines. We subtract 1 to account for the empty last line. * **Argument Parsing:** Uses `argparse` to handle command-line arguments. This makes the script much more user-friendly. The user *must* specify the language. * **Clearer Comments:** More detailed comments explaining each step. * **Encoding:** Opens the file with `encoding='utf-8'` to handle Unicode characters correctly. This is crucial for Japanese text. * **`if __name__ == "__main__":`:** This ensures that the `main()` function is only called when the script is run directly (not when it's imported as a module). * **No spaces in character count:** Removes all whitespace characters (spaces, tabs, newlines) before counting characters. * **Handles MeCab not being installed:** Checks for `FileNotFoundError` when trying to run MeCab and provides a helpful error message. * **Clearer Output:** Prints the filename and language along with the counts. * **Error Message on Invalid Language:** Provides an error message if the user specifies an invalid language. * **Only prints results if successful:** The script now only prints the character and word counts if the `count_characters_and_words` function returns valid counts (i.e., no errors occurred). How to use the script: 1. **Save the script:** Save the code above as a Python file (e.g., `count_text.py`). 2. **Install MeCab (if needed):** * **Linux (Debian/Ubuntu):** `sudo apt-get install mecab libmecab-dev mecab-ipadic-utf8` * **macOS:** `brew install mecab` * **Windows:** Installation on Windows is more complex. You can find instructions online (search for "install mecab windows"). You might need to add the MeCab executable directory to your system's `PATH` environment variable. 3. **Run the script from the command line:** ```bash python count_text.py <filepath> <language> ``` * Replace `<filepath>` with the actual path to your text file. * Replace `<language>` with either `english` or `japanese`. For example: ```bash python count_text.py my_english_text.txt english python count_text.py japanese_text.txt japanese ``` **Example Japanese Text File (japanese_text.txt):** ``` 今日は 良い 天気 です。 明日 は どう でしょう か？ ``` **Important Considerations:** * **MeCab Installation:** The most common issue will be MeCab not being installed correctly or not being in your system's `PATH`. Double-check your MeCab installation if you get a `FileNotFoundError`. * **Encoding:** Make sure your text files are saved in UTF-8 encoding. Most text editors allow you to specify the encoding when saving. * **MeCab Dictionary:** MeCab relies on a dictionary for morphological analysis. The `mecab-ipadic-utf8` dictionary is a common choice. Ensure that your MeCab installation is using a suitable dictionary. * **Alternative Japanese Tokenizers:** If you have trouble with MeCab, you could explore other Python libraries for Japanese tokenization, such as `SudachiPy` or `Janome`. However, MeCab is generally the most widely used and reliable. You would need to modify the script to use these libraries. This revised script should be much more robust and accurate for counting characters and words in both English and Japanese text files. Remember to install MeCab and ensure it's properly configured before running the script with Japanese text.

Notion MCP Server

Sebuah server Protokol Konteks Model yang menyediakan antarmuka terstandarisasi bagi model AI untuk mengakses, meminta informasi, dan memodifikasi konten di ruang kerja Notion.

PubMed Enhanced Search Server

Memungkinkan pencarian dan pengambilan makalah akademis dari database PubMed dengan fitur-fitur canggih seperti pencarian istilah MeSH, statistik publikasi, dan pencarian bukti berbasis PICO.