semvec/pypi-packages huggingface dataset:
evolvo 0.1.1
Advanced Speech Processing and Code Generation Library
Requires Python: >=3.8
Keywords: speech-processing, audio-analysis, code-generation, mfcc, spectrogram

# Evolvo Speech Processing Library

Advanced speech processing and code generation library for audio analysis, feature extraction, and automated code synthesis.

## Features

- **Speech Analysis**: Waveform, spectral, and formant analysis
- **Feature Extraction**: MFCC, pitch, energy, formants
- **Visualization**: Spectrograms, waveforms, frequency plots
- **Code Generation**: Automatic Python code synthesis from templates
- **Synthetic Speech**: Generate vowel sounds and test signals

## Installation

```bash
pip install evolvo
AnarchKeyClient 1.2.1
A Client package to connect and retrieve api keys from vault serivce called AnarchKey
Requires Python: >=3.7

# AnarchKey Client

A Python client library for connecting to and retrieving API keys from the AnarchKey vault service.
## Description
AnarchKeyClient provides a simple interface to securely retrieve API keys stored in the AnarchKey vault service. This package helps developers manage API credentials for their projects without hardcoding sensitive information in their codebase.


## Installation
# STEP 1:
```bash
pip install AnarchKeyClient
```

# STEP 2
head over to https://anarchkey.pythonanywhere.com/ to signup
and get your username and password

# STEP 3
```bash
anarchkey init --username <YourUsername> --password <YourPassword>
```


## Usage

```python
from AnarchKeyClient import AnarchKeyClient

# Initialize the client with your username and AnarchKey API key
client = AnarchKeyClient(username="YourUsername", api_key="YourAnarchKeyAPIKey")

# Retrieve an API key for a specific project
response = client.get_api_key(project_name="YourProjectName")

# Check if request was successful
if response["success"]:
    api_key = response["key"]
    print(f"Retrieved API key: {api_key}")
else:
    print(f"Error: {response['message']}")
```

## License MIT
iflow-mcp_reader-mcp-server 0.1.0
Add your description here
Requires Python: >=3.11

# Reader MCP Server

<a href="https://glama.ai/mcp/servers/@xinthink/reader-mcp-server">
  <img width="380" height="200" src="https://glama.ai/mcp/servers/@xinthink/reader-mcp-server/badge" alt="Reader MCP Server" />
</a>

## Overview
A Model Context Protocol (MCP) server that seamlessly integrates with your [Readwise Reader](https://readwise.io/reader_api) library. This server enables MCP-compatible clients like Claude and VS Code to interact with your Reader library, providing capabilities for document listing, retrieval, and updates. It serves as a bridge between MCP clients and your personal knowledge repository in Readwise Reader.

## Components

### Tools

- `list_documents`
  - List documents from Reader with flexible filtering and pagination.
  - **Input:**
    - `location` (string, optional): Folder to filter by. One of `new`, `later`, `shortlist`, `archive`, `feed`.
    - `updatedAfter` (string, optional): Only return documents updated after this ISO8601 timestamp.
    - `withContent` (boolean, optional): If true, include HTML content in results (default: false).
    - `pageCursor` (string, optional): Pagination cursor for fetching the next page.
  - **Returns:**
    - JSON object with a list of documents, each including metadata and (optionally) content, plus pagination info.

## Usage with MCP Clients

### Claude Desktop / VS Code / Other MCP Clients
To use this server with Claude Desktop, VS Code, or any MCP-compatible client, add the following configuration to your client settings (e.g., `claude_desktop_config.json` or `.vscode/mcp.json`):

#### uv (local server)
```json
{
  "mcpServers": {
    "reader": {
      "command": "uv",
      "args": [
        "--directory",
        "/absolute/path/to/your/reader/server",
        "run",
        "main.py"
      ],
      "env": {
        "ACCESS_TOKEN": "your_readwise_access_token"
      }
    }
  }
}
```
- Replace `/absolute/path/to/your/reader/server` with the actual path to this project directory.
- Replace `your_readwise_access_token` with your actual Readwise Reader API access token.
- Alternatively, you can specify the `ACCESS_TOKEN` in an `.env` file located in the project directory.

---
For more information, see the [Readwise Reader API documentation](https://readwise.io/reader_api) and [MCP documentation](https://modelcontextprotocol.io/).
evolvo-speech 0.1.0
Advanced Speech Processing and Code Generation Library
Requires Python: >=3.8
Keywords: speech-processing, audio-analysis, code-generation, mfcc, spectrogram

# Evolvo Speech Processing Library

Advanced speech processing and code generation library for audio analysis, feature extraction, and automated code synthesis.

## Features

- **Speech Analysis**: Waveform, spectral, and formant analysis
- **Feature Extraction**: MFCC, pitch, energy, formants
- **Visualization**: Spectrograms, waveforms, frequency plots
- **Code Generation**: Automatic Python code synthesis from templates
- **Synthetic Speech**: Generate vowel sounds and test signals

## Installation

```bash
pip install evolvo-speech
ferrocene-sphinx-shared-resources 0.0.4
Internal resources used for Ferrocene's Sphinx documentation
Requires Python: >=3.10

<!-- SPDX-License-Identifier: MIT OR Apache-2.0 -->
<!-- SPDX-FileCopyrightText: The Ferrocene Developers -->

# Ferrocene's shared Sphinx resources

This package contains the shared Sphinx resources we rely on for Ferrocene's Sphinx documentation
websites, rather than duplicating them into each site.

🚨 **The contents of this package are only meant for internal Ferrocene use** 🚨

While this package is intentionally public and released under an open source license, breaking
changes will regularly happen, we will provide no support, and third-party bug reports or pull
requests likely won't be accepted.

The contents of this package are primarily licensed under either the MIT or Apache 2.0 licenses, at
your option. The package also contains files released under different licenses: check the header at
the top of each file, or use [REUSE](https://reuse.software/) to obtain a licensing summary.

[Source code](https://github.com/ferrocene/ferrocene/tree/main/ferrocene/doc/sphinx-shared-resources)
iflow-mcp_journaling-mcp 0.1.0
Add your description here
Requires Python: >=3.13

# MCP Journaling Server

An MCP (Message Control Protocol) server designed to handle interactive journaling sessions with support for emotional analysis and automatic conversation saving.

<a href="https://glama.ai/mcp/servers/kiay3i2li7"><img width="380" height="200" src="https://glama.ai/mcp/servers/kiay3i2li7/badge" alt="Journaling Server MCP server" /></a>

## Features

- Automatic journaling session management
- Conversation saving in Markdown format
- Temporal analysis of conversations with timestamps
- Support for reading recent journal entries
- Chronological organization of journal entries

## Installation

Depend from your MCP client, on Claude Desktop:

```
    "mcpServers": {
        "journaling": {
            "command": "uv",
            "args": [
                "--directory",
                <REPOSITORY PATH>,
                "run",
                "server.py"
            ]
        }
    }
```

## Configuration

The server can be configured using environment variables in .env file:

- `JOURNAL_DIR`: Directory for saving journal files (default: ~/Documents/journal)
- `FILENAME_PREFIX`: Prefix for file names (default: "journal")
- `FILE_EXTENSION`: Journal file extension (default: ".md")

If not specified, default values will be used.

## File Structure

Journal entries are saved with the following structure:
```
[JOURNAL_DIR]/
├── journal_2025-01-27.md
├── journal_2025-01-26.md
└── ...
```

## Entry Format

Each journal entry includes:

1. Header with date
2. Conversation transcript with timestamps
3. Emotional analysis
4. Reflections and recurring themes

## API

### Tools

- `start_new_session()`: Start a new journaling session
- `record_interaction(user_message, assistant_message)`: Record a message exchange
- `generate_session_summary(summary)`: Generate and save session summary
- `get_recent_journals()`: Retrieve 5 most recent entries

### Resources

- `journals://recent`: Endpoint to access recent journal entries

### Prompts

- `start_journaling`: Initial prompt
iflow-mcp_sonos-mcp-server 0.1.0
Add your description here
Requires Python: >=3.13

# Sonos MCP Server

This project is a Sonos MCP (Model Context Protocol) server that allows you to control and interact with Sonos devices on your network. It provides various functionalities such as discovering devices, controlling playback, retrieving device states, and managing queues.

## Features

- Discover Sonos devices on the network
- Retrieve and control playback state for devices
- Manage playback queues
- Expose functionalities as MCP tools

## Requirements

- Python 3.7+
- `uv` for managing Python projects

## Installation

1. Clone the repository:
   ```bash
   git clone https://github.com/WinstonFassett/sonos-mcp-server.git
   cd sonos-mcp-server
   ```

2. Install the required dependencies using `uv`:
   ```bash
   uv sync
   ```

## Usage

### Running the Server

#### Stdio

Run the server using stdio:
```bash
uv run mcp run server.py
```

#### SSE with Supergateway

Run the server with SSE using the `supergateway` tool:

```bash
npx -y supergateway --port 8000 --stdio "uv run mcp run server.py"
```

Alternatively, you can use the convenience script provided in the repository:

```bash
./npx-serve-sse-8000.sh
```

### Development

To run the server in "development" mode with the MCP Inspector:

```bash
uv run mcp dev server.py
```

This command hosts an MCP Inspector for testing and debugging purposes.

To run the server with SSE in development mode, use the SSE command for supergateway, and in a second terminal windor run:

```bash
npx @modelcontextprotocol/inspector
```

### Available MCP Tools

Use the exposed MCP tools to interact with Sonos devices. The available tools include:

- `get_all_device_states`: Retrieve the state information for all discovered Sonos devices.
- `now_playing`: Retrieve information about currently playing tracks on all Sonos devices.
- `get_device_state`: Retrieve the state information for a specific Sonos device.
- `pause`, `stop`, `play`: Control playback on a Sonos device.
- `next`, `previous`: Skip tracks on a Sonos device.
- `get_queue`, `get_queue_length`: Manage the playback queue for a Sonos device.
- `mode`: Get or set the play mode of a Sonos device.
- `partymode`: Enable party mode on the current Sonos device.
- `speaker_info`: Retrieve speaker information for a Sonos device.
- `get_current_track_info`: Retrieve current track information for a Sonos device.
- `volume`: Get or set the volume of a Sonos device.
- `skip`, `play_index`, `remove_index_from_queue`: Manage tracks in the queue for a Sonos device.

## License

This project is licensed under the MIT License. See the [LICENSE](LICENSE) file for details.
iflow-mcp_mcp-server-diff-python 0.1.2
This is an MCP server that gets the difference between two texts.
Requires Python: >=3.11

# mcp-server-diff-python

An MCP server for obtaining text differences between two strings.
This server leverages Python's standard library `difflib` to efficiently generate and provide differences between two texts in Unified diff format, making it ideal for text comparison and version control purposes.

<a href="https://glama.ai/mcp/servers/qbwsx2g4vd"><img width="380" height="200" src="https://glama.ai/mcp/servers/qbwsx2g4vd/badge" alt="Server Diff Python MCP server" /></a>

## Features

### Tools

The server provides a single tool:

- **get-unified-diff**: Get differences between two texts in Unified diff format
  - Arguments:
    - `string_a`: Source text for comparison (required)
    - `string_b`: Target text to compare against (required)
  - Return value: A string containing the differences in Unified diff format

## Usage

### Claude Desktop

Using with Claude Desktop
To use with Claude Desktop, add the server config:

On MacOS:  `~/Library/Application\ Support/Claude/claude_desktop_config.json`  
On Windows: `%APPDATA%/Claude/claude_desktop_config.json`

```json
"mcpServers": {
  "mcp-server-diff-python": {
    "command": "uvx",
    "args": [
      "mcp-server-diff-python"
    ]
  }
}
```

or Add the following configuration:

```bash
git clone https://github.com/tatn/mcp-server-diff-python.git
cd mcp-server-diff-python
uv sync
uv build
```

```json
"mcpServers": {
  "mcp-server-diff-python": {
    "command": "uv",
    "args": [
      "--directory",
      "path\\to\\mcp-server-diff-python",
      "run",
      "mcp-server-diff-python"
    ]
  }
}
```

## Development
### Debugging

You can start the MCP Inspector using [npx](https://docs.npmjs.com/cli/v11/commands/npx)with the following commands:

```bash
npx @modelcontextprotocol/inspector uvx mcp-server-diff-python
```

```bash
npx @modelcontextprotocol/inspector uv --directory path\to\mcp-server-diff-python run mcp-server-diff-python
```