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ComfyUI MCP Server (local image/video generation)

Warning: Requires a powerful GPU!

A containerized ComfyUI setup with MCP (Model Context Protocol) integration for AI-driven image generation workflows. Includes both standard MCP (stdio) access and HTTP API access.

Usage

See the template repository for a complete example. Also includes the model training mcp used for creating checkpoints / loras.

MCP Server Manim Integration

A complete implementation guide for integrating Manim (Mathematical Animation Engine) with MCP (Model Context Protocol) servers, enabling AI assistants to create programmatic animations and visualizations.

Example Output

Gemini AI Integration MCP Server

A Model Context Protocol (MCP) server that integrates Google's Gemini AI for code review, technical consultation, and AI-assisted development workflows. This server provides seamless integration with Claude Code and other MCP-compatible clients.

Usage

Code Bash command prefix detection

This defines risk levels for actions that the ${K4} agent may take. This classification system is part of a broader safety framework and is used to determine when additional user confirmation or oversight may be needed.

Command prefix extraction examples

Examples:

cat foo.txt => cat
cd src => cd

Which GGUF is right for me? (Opinionated)

Good question! I am collecting human data on how quantization affects outputs. See here for more information: ggml-org/llama.cpp#5962

In the meantime, use the largest that fully fits in your GPU. If you can comfortably fit Q4_K_S, try using a model with more parameters.

llama.cpp feature matrix

See the wiki upstream: https://github.com/ggerganov/llama.cpp/wiki/Feature-matrix

	import os
	import json
	import aiohttp
	import datetime
	from contextlib import asynccontextmanager
	from typing import AsyncIterator
	from mcp.server.fastmcp import FastMCP, Context
	from mcp.server import Server
	from swarm import Swarm, Agent
	from duckduckgo_search import DDGS

	You are Manus, an AI agent created by the Manus team.

	You excel at the following tasks:
	1. Information gathering, fact-checking, and documentation
	2. Data processing, analysis, and visualization
	3. Writing multi-chapter articles and in-depth research reports
	4. Creating websites, applications, and tools
	5. Using programming to solve various problems beyond development
	6. Various tasks that can be accomplished using computers and the internet

	# train_grpo.py
	#
	# See https://github.com/willccbb/verifiers for ongoing developments
	#
	"""
	citation:

	@misc{brown2025grpodemo,
	title={Granular Format Rewards for Eliciting Mathematical Reasoning Capabilities in Small Language Models},
	author={Brown, William},

	from diffusers import FluxPipeline, AutoencoderKL
	from diffusers.image_processor import VaeImageProcessor
	from transformers import T5EncoderModel, T5TokenizerFast, CLIPTokenizer, CLIPTextModel
	import torch
	import gc


	def flush():
	gc.collect()
	torch.cuda.empty_cache()