semvec/pypi-packages huggingface dataset:
pexels-mcp-server 0.0.4
An MCP server for Pexels
Requires Python: >=3.11
Keywords: pexels, mcp, server, photos, videos

# Pexels MCP Server

A Model Context Protocol server that provides access to the **Pexels API** for searching and retrieving photos, videos, and collections.

## Available Tools

- `photos_search` – Search photos
- `photos_curated` – List curated photos
- `photo_get` – Get a photo by id
- `videos_search` – Search videos
- `videos_popular` – List popular videos
- `video_get` – Get a video by id
- `collections_featured` – List featured collections
- `collections_media` – List media in a collection


## Usage


### Using `uv` (recommended)

1. Install [`uv`](https://docs.astral.sh/uv/).

2. In your MCP client code configuration or **Claude** settings (file `claude_desktop_config.json`) add `pexels` mcp server:
    ```json
    {
        "mcpServers": {
            "pexels": {
                "command": "uvx",
                "args": ["pexels-mcp-server"],
                "env": {
                    "PEXELS_API_KEY": "<Your Pexels API key>"
                }
            }
        }
    }
    ```
    `uv` will download the MCP server automatically using `uvx` from [pypi.org](https://pypi.org/project/pexels-mcp-server/) and apply to your MCP client.

### Using `pip` for a project
1. Add `pexels-mcp-server` to your MCP client code `requirements.txt` file.
    ```txt
    pexels-mcp-server
    ```

2. Install the dependencies.
    ```shell
    pip install -r requirements.txt
    ```

3. Add the configuration for your client:
    ```json
    {
        "mcpServers": {
            "pexels": {
                "command": "python3",
                "args": ["-m", "pexels_mcp_server"],
                "env": {
                    "PEXELS_API_KEY": "<Your Pexels API key>"
                }
            }
        }
    }
    ```


### Using `pip` globally

1. Ensure `pip` or `pip3` is available on your system.
    ```bash
    pip install pexels-mcp-server
    # or
    pip3 install pexels-mcp-server
    ```

2. MCP client code configuration or **Claude** settings, add `pexels` mcp server:
    ```json
    {
        "mcpServers": {
            "pexels": {
                "command": "python3",
                "args": ["pexels-mcp-server"],
                "env": {
                    "PEXELS_API_KEY": "<Your Pexels API key>"
                }
            }
        }
    }
    ```


## Debugging

You can use the MCP inspector to debug the server. For `uvx` installations:

```bash
npx @modelcontextprotocol/inspector uvx pexels-mcp-server
```

Or if you've installed the package in a specific directory or are developing on it:

```bash
git clone https://github.com/garylab/pexels-mcp-server.git
cd pexels-mcp-server
npx @modelcontextprotocol/inspector uv run pexels-mcp-server -e PEXELS_API_KEY=<the key>
```


## License

pexels-mcp-server is licensed under the MIT License. This means you are free to use, modify, and distribute the software, subject to the terms and conditions of the MIT License. For more details, please see the LICENSE file in the project repository.
pycustomnets 1.1.0
A Neural Network customization tool
Requires Python: >=3.8

# pycustomnets
This is a little project that I've been working on for a while with the primary goal of gaining a more in depth understanding of deep learning and honing various skills

This is meant to be my version of tensorflow and while its definitely not as good as tensorflow for now, It does have some similar basic functions.

This project is also a package which can be downloaded through pip (however it might not very up to date)

`pip install pycustomnets`

IMPORTANT NOTE: If you plan on using this, expected values and input values must be arrays or else the model won't work. Single value expected values can be represented as a single element array (`[0]`, `[1]`, etc)

Activation functions
  - Leaky Relu ("lrelu")
  - Relu ("relu")
  - Sigmoid ("sigmoid")
  - Softmax ("softmax")

Error Functions
  - Mean Squared Error ("mse")
  - Cross Entropy ("cross_entropy")
  - Binary Cross Entropy ("b_cross_entropy")

Optimization algorithms
  - Stochastic Gradient Descent ("sgd")
  - ADAM ("adam")

In the future, I hope to add Reinforcement Learning algorithms, transformers and other stuff.

Example usage of pycustomnets can be found in the examples folder:
  - linearfunction: Trains a neural network to mimic a function
  - MNIST_Digits: Trains a neural network to identify handwritten digits from the mnist data base (Network architecture (784, 128, 10) was made similar to example shown in                   tensorflow demo for comparison purposes)

Convolutional Neural Networks have been added. Example usage coming soon

Better readme coming eventually