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MCP server · Science

AI Optimizer MCP

A multi-task MCP server for AI code optimization and testing that integrates with VSCode/Cursor, CLI tools, and autonomous agents. It provides tools for running tests, generating code improvements, and managing objectives across different environments.

Unclaimed science
34Low

Scored 6 months ago · breakdown

About AI Optimizer MCP

AI Optimizer MCP is an MCP server in the Science category: a multi-task MCP server for AI code optimization and testing that integrates with VSCode/Cursor, CLI tools, and autonomous agents. It provides tools for running tests, generating code improvements, and managing objectives across different environments.

Install

From source
git clone https://github.com/BarackNdenga/AI-Task-Optimizer-MCP

This server has no ConduID identity, so agent calls to it are not receipted. Pin the version you install and review the source before granting it credentials.

Tools (3)

generate_improvement`code, test_result`

Parameters not published.

get_objective-

Parameters not published.

run_tests`code_snippet: str`

Parameters not published.

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README

AI Optimizer MCP 🧠🔧 - Multi-Task MCP Server

Développer par Barack Ndenga ♥️

PyPI version Tests Coverage

Détails

Multi-tâche MCP Server pour VSCode/Cursor, CLI, agents autonomes. Optimisation code IA + tests + extensible.

  • Transports: Stdio (VSCode), subprocess, HTTP (futur)
  • Use Cases: VSCode chat, agent loops, CI/CD, remote servers
  • Sécurité: Env vars, sandbox exec

Manifeste (Multi-Task Capabilities)

  • 🛠️ 3+ Tools: Code test/optimize/objective (+extensibles)
  • 🔌 VSCode/Cursor: mcp.json natif
  • 🖥️ CLI Standalone: ai-optimizer-mcp run
  • 🤖 Agents: examples/agent.py loop
  • ⚙️ Multi-Env: Local/dev/prod via .env
  • 📊 Memory/History: JSON persistent
  • 🔄 Boucles Itératives: Auto-improve

Configuration Multi-Plateforme

1. VSCode/Cursor (Recommandé)

Fichier .vscode/mcp.json (multi-servers):

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

Multi-task: Switch servers en chat!

2. CLI / Scripts / Agents

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

3. Agents Autonomes / 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})

Prérequis (.env)

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

Usage Multi-Tâche

  1. VSCode Chat: use_mcp_tool("ai-optimizer", "run_tests", ...)
  2. CLI Pipe: echo code | ai-optimizer-mcp run
  3. Agent Loop: python examples/agent.py
  4. CI/CD: Subprocess dans GitHub Actions/Jenkins

Exemple Réponse Tool:

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

Troubleshooting Multi-Env

  • VSCode: Reload window après mcp.json
  • No API Key: ValueError → Check .env
  • Timeout: TEST_TIMEOUT=10 in .env
  • Memory: rm memory.json
  • Logs: --dev ou LOG_LEVEL=DEBUG

Développement

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

Tools MCP (Extensibles)

Tool Args Use Case
run_tests code_snippet: str VSCode/CLI test code
generate_improvement code, test_result Auto-optimize
get_objective - Read goal any context

Apache 2.0 - Multi-task ready! VSCode, CLI, Agents, CI. Contribute!

CHANGELOG

README copy captured when this server was indexed; it may lag the repository. The original is authoritative.

Questions

About AI Optimizer MCP

How do I install AI Optimizer MCP?

No package has been published for this server, so install it from source: clone the repository and follow its README.

Is AI Optimizer MCP safe to use with an AI agent?

Its trust score is 34 out of 100 (low). It passes 1 of the 1 static security checks run so far. It has no ConduID identity yet, so agent calls to it are not receipted.

Is AI Optimizer MCP still maintained?

Conduid hasn't recorded a commit date for this repository yet. Check the repository directly for recent activity.