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MCP SSE demo

demo of MCP SSE server limitations using the bun runtime

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.

mcp-excalidraw

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

MCP Node.js Debugger

Memungkinkan Claude untuk secara langsung melakukan debug pada server NodeJS dengan mengatur breakpoint, memeriksa variabel, dan menelusuri kode.

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.

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.

Kafka MCP Server

Memungkinkan model AI untuk mempublikasikan dan mengonsumsi pesan dari topik Apache Kafka melalui antarmuka yang terstandardisasi, sehingga memudahkan integrasi pesan Kafka dengan aplikasi LLM dan agen.

quickchart-server MCP Server

Sebuah server MCP untuk menghasilkan visualisasi data yang dapat disesuaikan menggunakan QuickChart.io, mendukung berbagai jenis grafik dan konfigurasi Chart.js.

reddit-mcp

MCP server for reddit.

For the GitHub MCP

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

➡️ browser-use mcp server

Sebuah server MCP yang memungkinkan asisten AI untuk mengendalikan peramban web melalui perintah bahasa alami, memungkinkan mereka untuk menavigasi situs web dan mengekstrak informasi melalui transportasi SSE.

MCP Server Gateway

A gateway demo for MCP SSE Server

Linear MCP Server

Sebuah server yang memungkinkan asisten AI untuk mengakses dan mengambil data tiket Linear melalui standar Model Context Protocol (MCP), saat ini berfokus pada pengambilan tiket todo pengguna.

OneSignal MCP Server

Server Protokol Konteks Model yang membungkus OneSignal REST API, memungkinkan pengelolaan notifikasi push, email, SMS, perangkat pengguna, dan segmen di berbagai aplikasi OneSignal.

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.

filesystem

Server Protokol Konteks Model yang memperluas kemampuan AI dengan menyediakan akses sistem berkas dan fungsionalitas manajemen ke Claude atau asisten AI lainnya.

openpyxl_mcp_server

Pembungkus tipis di sekitar pustaka OpenPyXl Python yang mengekspos operasi file Excel sebagai server Model Context Protocol (MCP), memungkinkan Claude dan klien MCP lainnya untuk mengambil dan menganalisis data dari file Excel.

Legion MCP (Model Context Protocol) Server

Sebuah server yang membantu orang mengakses dan menanyakan data dalam basis data menggunakan Query Runner dengan integrasi Model Context Protocol (MCP) Python SDK. Mendukung basis data termasuk: PostgreSQL Redshift MySQL Microsoft SQL Server Google APIs Amazon Web Services (melalui boto3) CockroachDB SQLite

OpenAPI MCP Server

Alat ini membuat server Model Context Protocol (MCP) yang bertindak sebagai proksi untuk API apa pun yang memiliki spesifikasi OpenAPI v3.1. Ini memungkinkan Anda menggunakan Claude Desktop untuk berinteraksi dengan mudah dengan API server lokal maupun jarak jauh.