What is Experiments With Mcp

Experiments with MCP is a repository that serves as a collection of practical experiments utilizing the MCP framework, focusing more on applied aspects rather than theoretical architecture.

Use cases

Use cases include generating images from text prompts, experimenting with local language models, and developing custom AI agents for various tasks.

How to use

To use experiments-with-mcp, clone the repository and follow the instructions for either TypeScript or Python. For TypeScript, run examples using ‘@huggingface/tiny-agents’, and for Python, install ‘huggingface_hub[mcp]’ and execute the examples provided.

Key features

Key features include the ability to run local models using llama.cpp, easy integration with Hugging Face Inference Providers, and a variety of example applications for quick experimentation.

Where to use

Experiments with MCP can be used in fields such as AI development, machine learning research, and any application requiring natural language processing or image generation capabilities.

Experiments with MCP

At this point everyone and their mum’s are talking about MCP, this repo is just a collection of experiments with it.

Mostly focused around parctical and applied aspects of MCP than theory/ architecture behind.

Getting Started

The simplest way is to use a simple client/ library that allows you to get your feet wet as soon as possible.

I’m biased but some of the ways I recommend trying is:

1. @huggingface/tiny-agents (for TS fans)
2. huggingface_hub[mcp] (for python fans)

Let’s get started:

Step 1: Clone this repo

git clone https://github.com/Vaibhavs10/experiments-with-mcp && cd experiments-with-mcp

Step 2 (TS): Try any of the examples

For example you can run the image-gen example like this:

npx @huggingface/tiny-agents run ./image-gen

Step 2 (Python):

uv pip install "huggingface_hub[mcp]>=0.32.0"

tiny-agents run ./image-gen

Using Local models w/ Llama.cpp

In the examples above we used hosted models via Hugging Face Inference Providers but in reality you can use any tool calling enabled LLM (even those running locally).

Arguably the best way to run local models is llama.cpp

On a mac, you can install it via:

brew install llama.cpp

Once installed you can use any LLMs

llama-server --jinja -fa -hf unsloth/Qwen3-30B-A3B-GGUF:Q4_K_M -c 16384

Once the server is up, you can call tiny agents.

The only change you need is in the agents.json file

{
	"model": "unsloth/Qwen3-30B-A3B-GGUF:Q4_K_M",
+	"endpointUrl": "http://localhost:8080/v1",
-	"provider": "nebius",

	"servers": [
		{
			"type": "sse",
			"config": {
				"url": "https://evalstate-flux1-schnell.hf.space/gradio_api/mcp/sse"
			}
		}
	]
}

That’s it, you can now run your agent directly!

npx @huggingface/tiny-agents run ./local-image-gen

and… you can do the same thing via huggingface_hub MCPClient too:

tiny-agents run ./local-image-gen

That’s it! go ahead, give it a shot!

Using Local models for complex workflows