AI Optimizer MCP
Enables integration with GitHub Actions for CI/CD pipelines, allowing automated code testing and optimization workflows through subprocess execution.
Supports integration with Jenkins for CI/CD pipelines, enabling automated code testing and optimization workflows in Jenkins environments.
Integrates with OpenAI's API for AI-powered code optimization and improvement capabilities, requiring an OpenAI API key for functionality.
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., "@AI Optimizer MCPrun tests on this Python function that calculates factorial"
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.
AI Optimizer MCP 🧠🔧 - Multi-Task MCP Server
Developed by Barack Ndenga ♥️
Details
Multi-task MCP Server for VSCode/Cursor, CLI, autonomous agents. AI code optimization + testing + extensible.
Transports: Stdio (VSCode), subprocess, HTTP (future)
Use Cases: VSCode chat, agent loops, CI/CD, remote servers
Security: Env vars, sandbox exec
Related MCP server: VegaMCP
Manifesto (Multi-Task Capabilities)
🛠️ 3+ Tools: Code test/optimize/objective (+extensible)
🔌 VSCode/Cursor: native mcp.json
🖥️ CLI Standalone:
ai-optimizer-mcp run🤖 Agents: examples/agent.py loop
⚙️ Multi-Env: Local/dev/prod via .env
📊 Memory/History: Persistent JSON
🔄 Iterative Loops: Auto-improve
Multi-Platform Configuration
1. VSCode/Cursor (Recommended)
.vscode/mcp.json file (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 in 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.json3. Autonomous Agents / 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})Prerequisites (.env)
cp .env.example .env
# OPENAI_API_KEY=sk-...
# OBJECTIVE="Your custom goal"Multi-Task Usage
VSCode Chat:
use_mcp_tool("ai-optimizer", "run_tests", ...)CLI Pipe:
echo code | ai-optimizer-mcp runAgent Loop:
python examples/agent.pyCI/CD: Subprocess in GitHub Actions/Jenkins
Tool Response Example:
run_tests → "Tests passed: score=4/4 (f(2)=4)"
generate_improvement → "def f(x): return 2 * x"Multi-Env Troubleshooting
VSCode: Reload window after mcp.json
No API Key: ValueError → Check .env
Timeout:
TEST_TIMEOUT=10in .envMemory:
rm memory.jsonLogs:
--devorLOG_LEVEL=DEBUG
Development
pip install -e .[dev]
pre-commit install
pytestMCP Tools (Extensible)
Tool | Args | Use Case |
|
| VSCode/CLI test code |
|
| Auto-optimize |
| - | Read goal any context |
Apache 2.0 - Multi-task ready! VSCode, CLI, Agents, CI. Contribute!
This server cannot be deployed
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