Allows sending native desktop notifications specifically for the KDE environment on Linux systems.
Supports native system-level notifications on Linux to inform developers of process completions or updates.
Provides tools for fullstack development of Next.js applications, including reading and writing .tsx and .ts components.
Facilitates the development of Python applications through specialized tools for reading, writing, and managing .py source files.
Click on "Deploy Server".
Wait a few minutes for the server to deploy. Once ready, it will show a "Started" state.
In the chat, type @ followed by the MCP server name and your instructions, e.g., "@Professional Python MCP Server Create a Next.js Dashboard component and notify me when it's ready."
That's it! The server will respond to your query, and you can continue using it as needed.
Here is a step-by-step guide with screenshots.
Este repositorio contiene una implementación profesional de Model Context Protocol (MCP) diseñada para asistir en el desarrollo de aplicaciones Fullstack (Python y Next.js).
MCP Server (server.py): Implementado con FastMCP.
read_code: Lectura segura de archivos (.py, .ts, .tsx, etc).
write_code: Escritura y actualización de código.
run_terminal: Ejecución de comandos con captura de logs.
list_models: Descubrimiento de modelos Gemini disponibles.
notify_dev: Notificaciones nativas en Linux (KDE).
Utilidades:
check_models.py: Script independiente para verificar tu API Key y modelos.
MCP Agent (agent.py): Cliente inteligente usando Gemini 3 Flash (por defecto).
Selección interactiva de modelos al inicio.
Narración de pasos antes de ejecutar acciones.
Conector stdio para comunicación robusta con el servidor.
Related MCP server: Boring Gemini
Python 3.10+
uv (Gestor de paquetes y entornos)
Gemini API Key
Clonar el repositorio:
git clone <repository-url>
cd mcp_serverConfigurar el entorno:
uv venv --python 3.10
source .venv/bin/activateInstalar dependencias:
uv pip install -e .Configurar variables de entorno:
Crea un archivo .env basado en .env.example:
cp .env.example .env
# Edita .env y añade tu GEMINI_API_KEYEl agente iniciará automáticamente el servidor MCP en segundo plano.
uv run agent.py"Léeme el archivo server.py y explica qué hace cada herramienta."
"Crea un componente de Next.js llamado UserProfile.tsx que reciba un nombre y un email."
"Ejecuta ls -la y dime qué archivos hay en la raíz."
"Envíame una notificación diciendo que el despliegue ha terminado."
El servidor está configurado para ejecutarse mediante uv run server.py, lo que garantiza que las dependencias estén siempre actualizadas.
Si simplemente pulsas Enter, usará por defecto gemini-3-flash-preview (rápido y eficiente).
Se ha incluido un .gitignore profesional para evitar subir secretos o entornos virtuales.
Desarrollado con ❤️ para agilizar flujos de trabajo Fullstack.
Lists available Gemini models sorted by input token limit (context window). Provides a proxy for 'capacity' since exact quota is not available via API.
Inputs: no named parameters. Returns: result
Sends a desktop notification. Supports Linux (notify-send).
Inputs: message (required). Returns: result
Reads the content of a file. Supported extensions: .py, .ts, .tsx, .js, .json, .md.
Inputs: path (required). Returns: result
Executes a shell command and returns stdout and stderr. Example: npm run dev, pytest, etc.
Inputs: command (required). Returns: result
Writes or overwrites content to a file.
Inputs: path (required), content (required). Returns: result
Recent tool additions, removals, and schema changes observed during successful MCP inspections.
Loading tool schema changelog…
Scored across 5 tools
Each tool has a clearly distinct purpose with no overlap: list_models_with_limits handles model information, notify_dev manages notifications, read_code and write_code handle file I/O (one for reading, one for writing), and run_terminal executes shell commands. The descriptions reinforce these distinct roles, making misselection unlikely.
The tool names follow a consistent verb_noun pattern (e.g., list_models_with_limits, read_code, write_code, run_terminal), which is predictable and readable. The only minor deviation is notify_dev, which uses 'notify' as the verb instead of a more standard action like 'send_notification', but it still fits the overall pattern well.
With 5 tools, the count is reasonable for a server focused on Python development utilities, covering model listing, notifications, file operations, and terminal execution. It feels slightly thin but well-scoped, as each tool serves a clear purpose without unnecessary bloat.
The server covers basic development tasks like file I/O, command execution, and notifications, but there are notable gaps for a 'Professional Python' scope, such as missing tools for code analysis (e.g., linting, testing), dependency management, or environment handling. The surface is functional but incomplete for advanced workflows.
A full backend your AI coding agent can set up over MCP
LLM Orchestration Agent (Mcp)
GodPrompt MCP + Agent Skill for coding agents with TDD, debugging, verification, and task routing.
Build and publish full-stack apps from your coding agent: models, rules, pages, auth, per-app MCP.
