Skip to main content
Glama

AI Optimizer MCP 🧠🔧 - Multi-Task MCP Server

Developed by Barack Ndenga ♥️

PyPI version Tests Coverage

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

.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.json

3. 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

  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 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=10 in .env

  • Memory: rm memory.json

  • Logs: --dev or LOG_LEVEL=DEBUG

Development

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

MCP Tools (Extensible)

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

A
license - permissive license
Not graded
quality - not tested
D
maintenance

Maintenance

Maintainers
Response time
Release cycle
1Releases (12mo)
Commit activity

Resources

Unclaimed servers have limited discoverability.

Looking for Admin?

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

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.
    130
    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

View all related MCP servers

Related MCP Connectors

  • MCP server for AI agents to plan, verify, and deploy Cloudflare-native apps.

  • A comprehensive Model Context Protocol (MCP) server that enables AI assistants to interact with yo…

  • MCP server exposing the Backtest360 engine API as tools for AI agents.

View all MCP Connectors

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