Skip to main content
Glama

AI Optimizer MCP 🧠🔧 - Servidor MCP Multitarea

Desarrollado por Barack Ndenga ♥️

PyPI version Tests Coverage

Detalles

Servidor MCP multitarea para VSCode/Cursor, CLI y agentes autónomos. Optimización de código mediante IA + pruebas + extensible.

  • Transportes: Stdio (VSCode), subprocess, HTTP (futuro)

  • Casos de uso: Chat de VSCode, bucles de agentes, CI/CD, servidores remotos

  • Seguridad: Variables de entorno, ejecución en sandbox

Related MCP server: VegaMCP

Manifiesto (Capacidades multitarea)

  • 🛠️ 3+ Herramientas: Prueba/optimización de código/objetivo (+extensibles)

  • 🔌 VSCode/Cursor: mcp.json nativo

  • 🖥️ CLI independiente: ai-optimizer-mcp run

  • 🤖 Agentes: bucle en examples/agent.py

  • ⚙️ Entorno múltiple: Local/dev/prod mediante .env

  • 📊 Memoria/Historial: JSON persistente

  • 🔄 Bucles iterativos: Auto-mejora

Configuración multiplataforma

1. VSCode/Cursor (Recomendado)

Archivo .vscode/mcp.json (servidores múltiples):

{
  "servers": {
    "ai-optimizer": {
      "command": "python",
      "args": ["-m", "ai_optimizer_mcp.server"]
    },
    "ai-optimizer-dev": {
      "command": "python",
      "args": ["-m", "ai_optimizer_mcp.cli", "run", "--dev"]
    }
  }
}

Multitarea: ¡Cambia de servidor en el chat!

2. CLI / Scripts / Agentes

ai-optimizer-mcp run  # Stdio server (pipes)
ai-optimizer-mcp run --dev  # Debug
ai-optimizer-mcp --install-mcp  # Print mcp.json

3. Agentes autónomos / Subprocess

# examples/agent.py
import asyncio
from mcp.client.stdio import stdio_client

async def agent_loop():
    async with stdio_client(command=["python", "-m", "ai_optimizer_mcp.server"]) as client:
        # Multi-task calls
        score = await client.call_tool("run_tests", {"code_snippet": code})
        improved = await client.call_tool("generate_improvement", {"code": code, "test_result": score})

Requisitos previos (.env)

cp .env.example .env
# OPENAI_API_KEY=sk-...
# OBJECTIVE="Your custom goal"

Uso multitarea

  1. Chat de VSCode: use_mcp_tool("ai-optimizer", "run_tests", ...)

  2. CLI Pipe: echo code | ai-optimizer-mcp run

  3. Bucle de agente: python examples/agent.py

  4. CI/CD: Subprocess en GitHub Actions/Jenkins

Ejemplo de respuesta de herramienta:

run_tests → "Tests passed: score=4/4 (f(2)=4)"
generate_improvement → "def f(x): return 2 * x"

Solución de problemas en entornos múltiples

  • VSCode: Recarga la ventana después de modificar mcp.json

  • Sin clave API: ValueError → Comprueba .env

  • Tiempo de espera: TEST_TIMEOUT=10 en .env

  • Memoria: rm memory.json

  • Registros: --dev o LOG_LEVEL=DEBUG

Desarrollo

pip install -e .[dev]
pre-commit install
pytest

Herramientas MCP (Extensibles)

Herramienta

Argumentos

Caso de uso

run_tests

code_snippet: str

Probar código en VSCode/CLI

generate_improvement

code, test_result

Auto-optimización

get_objective

-

Leer objetivo en cualquier contexto

Apache 2.0 - ¡Listo para multitarea! VSCode, CLI, Agentes, CI. ¡Contribuye!

CHANGELOG

Tool Schema Changelog

Recent tool additions, removals, and schema changes observed during successful MCP inspections. Dates show when Glama detected each change.

No tool schema history has been recorded yet.

Maintenance

ActivityInactive
ResponsivenessSyncing

Resources

Unclaimed servers have limited discoverability.

Looking for Admin?

If you are the server author, to access and configure the admin panel.

Related MCP Connectors

Related MCP Servers

  • F
    license
    Not graded
    quality
    D
    maintenance
    A lightweight MCP server that enhances AI agents with tools for codebase analysis, task delegation to sub-agents, multi-agent coordination through chatrooms, and project todo management.
    -
  • A
    license
    Not graded
    quality
    D
    maintenance
    MCP server that enables AI coding agents to communicate, share state, and coordinate work in real time via MCP tools or REST API.
    159
    5
    MIT
  • A
    license
    Not graded
    quality
    B
    maintenance
    Multi-agent AI orchestrator that runs parallel coding agents in isolated sessions with self-improving intelligence, exposed via an MCP server for task execution and management.
    MIT

Latest Blog Posts

MCP directory API

We provide all the information about MCP servers via our MCP API.

curl -X GET 'https://glama.ai/api/mcp/v1/servers/BarackNdenga/AI-Task-Optimizer-MCP'

If you have feedback or need assistance with the MCP directory API, please join our Discord server