Discover Awesome MCP Servers

Algorand MCP Implementation

Un servidor MCP integral para interacciones de herramientas (más de 40) y accesibilidad de recursos (más de 60) con la cadena de bloques de Algorand, además de muchas indicaciones útiles.

Code Summarizer

Permite que herramientas de LLM como Claude Desktop y Cursor AI accedan y resuman archivos de código a través de un servidor de Protocolo de Contexto de Modelo, proporcionando acceso estructurado al contenido de la base de código sin necesidad de copiar manualmente.

Scast

Convierte código en diagramas UML y diagramas de flujo a través de análisis estático, permitiendo la visualización de la estructura del código y la explicación de la funcionalidad.

DeepView MCP

Un servidor de Protocolo de Contexto de Modelo que permite a IDEs como Cursor y Windsurf analizar grandes bases de código utilizando la extensa ventana de contexto de Gemini.

MCP Server for RSS3

Una implementación de servidor MCP que integra la API de RSS3, permitiendo a los usuarios consultar datos de cadenas descentralizadas, plataformas de redes sociales y la red RSS3 a través del lenguaje natural.

IDA Pro MCP Server

Un servidor de Protocolo de Contexto de Modelo que permite a los asistentes de IA interactuar con IDA Pro para tareas de ingeniería inversa y análisis de binarios.

memos-mcp-server

A MCP(Model Context Protocol) server for Memos.

datadog-mcp-server

MCP Server for Datadog

vrchat-mcp-osc

Proporciona un puente entre los asistentes de IA y VRChat, permitiendo el control de avatares impulsado por IA e interacciones en entornos de realidad virtual a través del Protocolo de Contexto de Modelo.

MCP Server for Milvus

Un servidor de integración que implementa el Protocolo de Contexto de Modelo (Model Context Protocol) y que permite a las aplicaciones LLM interactuar con la funcionalidad de la base de datos vectorial Milvus, permitiendo la búsqueda vectorial, la gestión de colecciones y las operaciones de datos a través del lenguaje natural.

LLMling

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

Jira MCP Server

Un servidor de Protocolo de Contexto de Modelo que permite a asistentes de IA como Claude interactuar con Jira, facilitando tareas de gestión de proyectos como listar proyectos, buscar incidencias, crear tickets y gestionar sprints a través de consultas en lenguaje natural.

AgentCraft MCP Server

Se integra con el marco de trabajo AgentCraft para permitir una comunicación segura y el intercambio de datos entre agentes de IA, admitiendo tanto agentes de IA empresariales predefinidos como personalizados.

mcp-osint OSINT Server

Servidor MCP para realizar varias tareas de OSINT aprovechando herramientas comunes de reconocimiento de redes.

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

Espejo de

MCP SSE demo

demo of MCP SSE server limitations using the bun runtime

ThemeParks.wiki API MCP Server

Servidor MCP de la API de ThemeParks.wiki

MCP SSH Server for Windsurf

MCP SSH server for Windsurf integration

S3 MCP Server

Un servidor de Protocolo de Contexto de Modelo de Amazon S3 que permite a los Modelos de Lenguaje Grandes como Claude interactuar con el almacenamiento de AWS S3, proporcionando herramientas para listar buckets, listar objetos y recuperar el contenido de los objetos.

mcp-server-cli

Model Context Protocol server to run shell scripts or commands

MCP Etherscan Server

Espejo de

G-Search MCP

Un potente servidor MCP que permite la búsqueda paralela en Google con múltiples palabras clave simultáneamente, proporcionando resultados estructurados a la vez que gestiona los CAPTCHAs y simula patrones de navegación de usuario.

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 help you.

Japanese Text Analyzer MCP Server

Okay, I understand. I can't directly *execute* code or interact with files on your system. However, I can provide you with a Python script that accomplishes the task you've described. You can then copy and paste this script into a Python environment on your computer and run it. Here's the Python script, along with explanations: ```python import os import re import argparse # For command-line arguments import subprocess # For calling MeCab import sys def count_characters_and_words(filepath, language): """ Counts characters (excluding spaces and line breaks) and words in a text file. Handles Japanese text with morphological analysis using MeCab. 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} with 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": try: # Use MeCab for morphological analysis 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 # Extract words from MeCab output (first element of each line before the tab) words = [line.split('\t')[0] for line in mecab_output.splitlines() if line.strip() != 'EOS'] word_count = len(words) # Count characters (excluding spaces and line breaks) characters = re.sub(r'\s', '', text) character_count = len(characters) except FileNotFoundError: print("Error: MeCab is not installed or not in your PATH.") return 0, 0 except Exception as e: print(f"An error occurred during MeCab processing: {e}") 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 parse command-line arguments and process files. """ parser = argparse.ArgumentParser(description="Count characters and words in text files.") 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/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 potential errors like `FileNotFoundError`, `UnicodeDecodeError` (if the file isn't UTF-8 encoded), and errors related to MeCab. This makes the script much more robust. It also checks for invalid language input. * **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 the standard output and standard error from MeCab, allowing for error reporting. The code now correctly parses the MeCab output to extract the words. It also checks if MeCab is installed. * **Character Counting:** Uses `re.sub(r'\s', '', text)` to remove all whitespace (spaces, tabs, newlines) before counting characters. This ensures accurate character counts as requested. * **UTF-8 Encoding:** Opens the file with `encoding='utf-8'` to handle Unicode characters correctly, which is essential for Japanese text. It also includes a check for `UnicodeDecodeError` and suggests trying a different encoding if UTF-8 fails. * **Command-Line Arguments:** Uses `argparse` to handle command-line arguments. This makes the script much more user-friendly. The user can specify the filepath and language directly when running the script. * **Clearer Output:** Prints the filename, language, character count, and word count in a clear and organized format. * **`if __name__ == "__main__":` block:** This ensures that the `main()` function is only called when the script is executed directly (not when it's imported as a module). * **Comments:** Includes detailed comments to explain the code. * **Handles Empty Files:** The `if char_count != 0 or word_count != 0:` check prevents printing output if the file was empty or an error occurred. * **MeCab Error Handling:** Specifically checks for errors returned by MeCab and prints them to the console. * **Correct MeCab Word Extraction:** The code now correctly extracts the words from the MeCab output by splitting each line at the tab character (`\t`) and taking the first element. It also filters out the "EOS" (End of Sentence) marker. 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 you haven't already):** * **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'll need to download the MeCab binaries and dictionaries. Refer to the MeCab documentation for Windows installation instructions. You might also need to set the `MECABRC` 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_file.txt english python count_text.py my_japanese_file.txt japanese ``` **Example:** Let's say you have a file named `japanese_text.txt` with the following content: ``` 今日は良い天気です。 明日はどうですか？ ``` And you run: ```bash python count_text.py japanese_text.txt japanese ``` The output would be similar to: ``` File: japanese_text.txt Language: japanese Character Count (excluding spaces/line breaks): 14 Word Count: 7 ``` **Important Considerations:** * **MeCab Installation:** Make sure MeCab is correctly installed and that the `mecab` command is accessible from your command line. If you get a "FileNotFoundError" for MeCab, it means Python can't find the MeCab executable. You might need to add the MeCab directory to your system's `PATH` environment variable. * **Encoding:** The script assumes UTF-8 encoding. If your file uses a different encoding, you'll need to change the `encoding='utf-8'` argument in the `open()` function accordingly. * **MeCab Dictionaries:** MeCab relies on dictionaries for morphological analysis. Make sure you have the appropriate dictionaries installed for Japanese. The `mecab-ipadic-utf8` package (on Debian/Ubuntu) provides a standard dictionary. * **Customization:** You can customize the script further to handle different languages, use different tokenizers, or perform more advanced text analysis. This comprehensive solution should meet your requirements for counting characters and words in both English and Japanese text files, with proper handling of Japanese morphological analysis and error handling. Remember to install MeCab and adjust the encoding if necessary.

Notion MCP Server

Un servidor de Protocolo de Contexto de Modelo que proporciona una interfaz estandarizada para que los modelos de IA accedan, consulten y modifiquen contenido en espacios de trabajo de Notion.

PubMed Enhanced Search Server

Permite la búsqueda y recuperación de artículos académicos de la base de datos PubMed con funciones avanzadas como la búsqueda de términos MeSH, estadísticas de publicación y búsqueda de evidencia basada en PICO.

Backstage MCP

A simple backstage mcp server using quarkus-backstage