---
title: "MCP core for an LLM assistant"
url: "https://ramenm.com/en/projects/enhanced-mcp-agent"
markdown_url: "https://ramenm.com/en/projects/enhanced-mcp-agent/index.md"
locale: "en"
content_language: "en"
page_kind: "project"
source: "localized_path"
llms_url: "https://ramenm.com/llms.txt"
llms_full_url: "https://ramenm.com/llms-full.txt"
---

# MCP core for an LLM assistant

- Case study URL: https://ramenm.com/en/projects/enhanced-mcp-agent
- Markdown URL: https://ramenm.com/en/projects/enhanced-mcp-agent/index.md
- Role: AI Tooling Developer
- Period: 2025
- Team: Solo

Backend core for an LLM assistant with plugin execution, hot reload, tool chains, and explicit context handoff.

## Problem
When an assistant grows beyond a demo, plugins and tool calls become hard to evolve without breaking the whole runtime.

## Solution
Built a FastAPI execution core with plugin lifecycle management, hot reload, cascading tool calls, and a CLI client with execution history.

## Impact
New tools can be added without rewriting the assistant core, and tool execution is easier to inspect.

## Stack
- Python, FastAPI, MCP, Tool calling, LLM APIs, Docker / Docker Compose, TypeScript

## Metrics
- Plugin model: Hot reload
- Execution: Context handoff
- Surface: CLI + API

## Highlights
- Implemented plugin hot reload without restarting the core process.
- Built explicit context handoff between chained tools.
- Separated runtime responsibilities from plugin responsibilities.

## Lessons
- AI tooling becomes useful when execution behavior is predictable.
- A clean plugin boundary matters more than the number of models connected.