semvec/pypi-packages huggingface dataset:
universal-mcp-fireflies 0.1.1
A Universal MCP Application: universal_mcp_fireflies
License: MIT
Requires Python: >=3.11

# Fireflies Universal MCP Server

This repository contains an implementation of an Fireflies Universal MCP (Model Context Protocol) server. It provides a standardized interface for interacting with Fireflies's SEO and backlink analysis tools through a unified API.

The server is built using the Universal MCP framework.

This implementation follows the MCP specification, ensuring compatibility with other MCP-compliant services and tools.

## Usage

You can start using Fireflies directly from [agentr.dev](https://agentr.dev). Visit [agentr.dev/apps](https://agentr.dev/apps) and enable Fireflies.

If you have not used universal mcp before follow the setup instructions at [agentr.dev/quickstart](https://agentr.dev/quickstart)

## Available Tools

The full list of available tools is at [./src/universal_mcp_fireflies/README.md](./src/universal_mcp_fireflies/README.md)

## Local Development

### 📋 Prerequisites

Ensure you have the following before you begin:

- **Python 3.11+** (recommended)
- **[uv](https://github.com/astral-sh/uv)** (install globally with `pip install uv`)

### 🛠️ Setup Instructions

Follow the steps below to set up your development environment:

1. **Sync Project Dependencies**

   ```bash
   uv sync
   ```

   This installs all dependencies from `pyproject.toml` into a local virtual environment (`.venv`).

2. **Activate the Virtual Environment**

   For Linux/macOS:

   ```bash
   source .venv/bin/activate
   ```

   For Windows (PowerShell):

   ```powershell
   .venv\Scripts\Activate
   ```

3. **Start the MCP Inspector**

   ```bash
   mcp dev src/universal_mcp_fireflies/server.py
   ```

   This will start the MCP inspector. Make note of the address and port shown in the console output.

4. **Install the Application**
   ```bash
   mcp install src/universal_mcp_fireflies/server.py
   ```

## 📁 Project Structure

```text
.
├── src/
│   └── universal_mcp_fireflies/
│       ├── __init__.py       # Package initializer
│       ├── server.py         # Server entry point
│       ├── app.py            # Application tools
│       └── README.md         # List of application tools
├── tests/                    # Test suite
├── .env                      # Environment variables for local development
├── pyproject.toml            # Project configuration
└── README.md                 # This file
```

## 📄 License

This project is licensed under the [MIT License](LICENSE).

---

_Generated with **MCP CLI** — Happy coding! 🚀_
sklearn-embeddings 0.1.3
Sentence-Transformer embeddings for scikit-learn
License: MIT
Requires Python: >=3.8

# sklearn-embeddings

## Overview
`sklearn-embeddings` is a Python package that integrates sentence-transformer based embeddings with scikit-learn classifiers and clustering algorithms. This allows users to leverage powerful natural language processing capabilities within the familiar scikit-learn framework.


## Installation
To install `sklearn-embeddings`, you can use pip:

```bash
pip install sklearn-embeddings
```


## Usage
Here is a simple example of how to use `sklearn-embeddings` with a scikit-learn classifier:

```python
import joblib

from sklearn_embeddings import SentenceTransformerEmbedding
from sklearn.linear_model import LogisticRegression
from sklearn.pipeline import make_pipeline

# Sample data
documents = ["The food was great.", "Not expensive and good service", "Not worth the money", "I've had better"]

# Labels
is_positive = [True, True, False, False]

# Create a pipeline with the embedding model and a classifier
pipeline = make_pipeline(
    SentenceTransformerEmbedding(), 
    # SentenceTransformerEmbedding('paraphrase-MiniLM-L6-v2'), 
    # SentenceTransformerEmbedding('/my/local/folder/paraphrase-MiniLM-L6-v2'), 
    # SentenceTransformerEmbedding(SentenceTransformer('paraphrase-MiniLM-L6-v2')), 
    LogisticRegression()
    )

# Fit the model
pipeline.fit(documents, is_positive)

# Make predictions
predictions = pipeline.predict(["So delicious!", "Not for me"])

# Write the whole pipeline to disk
joblib.dump(pipeline, 'model.joblib')

```

Perhaps the greatest benefit of this library is that it allows you to use Scikit-learn pipelines to combine encoding and labeling in a single function call.

```python
import joblib

model = joblib.load('model.joblib')

# Use the loaded pipeline as a simple model, it takes care of sentence-transformer encoding for you!
model.predict(["This is a sentence"])
```