semvec/pypi-packages huggingface dataset:
sumtyme 2.0.5
Python Client for Causal Intelligence Layer (CIL) by sumtyme.ai
Requires Python: >=3.12

## Embedded Intelligence Platform

A Python client for interacting with the **Embedded Intelligence Platform (EIP)**

## Getting Started

### Installation

You can install this package via pip.

```bash
pip install sumtyme
```

[API Documentation](https://docs.sumtyme.ai/)
TexTOM 0.7.16
A program for texture simulations.
Requires Python: <3.14,>=3.9

# TexTOM

Welcome to TexTOM, a package for analyzing data from texture tomography, a synchrotron radiation technique currently developed at Institut Fresnel in cooperation with the ESRF.

For information about the technique as well as the mathematical model and for citing, please refer to:
Frewein, M. P. K., Mason, J., Maier, B., Colfen, H., Medjahed, A., Burghammer, M., Allain, M. & Grunewald, T. A. (2024). IUCrJ, 11, 809-820. https://doi.org/10.1107/S2052252524006547

Upon installing, the following commands will be available in your environment:

textom - full program in ipython mode

textom_documentation - open documentation in pdf reader

textom_config - open config file in text editor

Version history:

0.6.0 - Modular arrangement of the model, diffractlet computation new and noiseless, grid based function in test phase. Limited compatibility with 0.5! 

0.5.x - improved optimizer, some fixes and new functions

0.4.11 - flip_fov moved to geometry.py - update your files before running data-import or alignment!

0.4.10 - Important fixes: 
    1) use export_paraview() to create a file for paraview, as the geometry import labels the axis differently (so, axes are switched in the old versions).
    2) the optimization of ghost HSHs was disabled. To import old optimization files, use load_opt(...,exclude_ghosts=False)

0.3.0 - version used at the TexTOM workshop 2025

0.2.x - versions tested on Ubuntu and OpenSUSE with data from ID13 (ESRF), some functions unstable

0.1.x - unstable versions
revengelibrary 0.2.1
Revenge Library
Requires Python: >=3.10
Keywords: chat, llm, openrouter, api, cli

# revengelibrary

Revenge Library

## Установка

```bash
python3 -m venv venv
source venv/bin/activate
```

```bash
pip install revengelibrary
```

## Запуск из терминала

После установки доступна команда:

```bash
revengelibrary
```
iflow-mcp_weekend-project-space-rss-mcp-server 0.1.0
A FastMCP server implementation for RSS feed management using the Fever API
Requires Python: >=3.8

# RSS MCP Server

[中文文档](README_zh.md)

A FastMCP server implementation for RSS feed management using the Fever API.

## Installation

1. Clone the repository
2. Install dependencies:

```bash
pip install -r requirements.txt
```

## Configuration

1. Copy `.env.example` to `.env`:

```bash
cp .env.example .env
```

2. Edit `.env` and set your configuration values:

- `FEVER_API_URL`: URL of your Fever API endpoint
- `FEVER_USERNAME`: Your Fever API username
- `FEVER_PASSWORD`: Your Fever API password
- `MCP_SERVER_NAME`: Name for your MCP server instance

3. Configure Cursor Integration:

Create or edit your Cursor MCP configuration file at `~/.cursor/mcp.json`:

```json
{
  "mcpServers": {
    "RssMcpServer": {
      "url": "http://127.0.0.1:8000/sse"
    }
  }
}
```

This configuration will allow Cursor to connect to your RSS MCP server running locally on port 8000.

## Running the Server

Start the server using:

```bash
python main.py
```

## API Tools

The server provides the following MCP tools:

### Feed Management

- `get_feeds()`: Retrieve all RSS feeds
- `get_groups()`: Retrieve all feed groups

### Item Operations

- `get_unread_items(max_items=10, max_id=9999999999, since_id="", feed_ids=None, group_ids=None)`: Get unread items
- `get_saved_items(max_items=10, max_id=9999999999)`: Get saved/favorited items
- `get_items(since_id="")`: Get items since a specific ID
- `get_items_by_feed_ids(feed_ids, max_items=10)`: Get items from specific feeds
- `get_items_by_group_ids(group_ids, max_items=10)`: Get items from specific groups
- `mark_item(item_id, action)`: Mark items as read/saved/unsaved

## Dependencies

- starlette>=0.27.0
- requests>=2.31.0
- python-dotenv>=1.0.0
- fastmcp>=0.1.0
- uvicorn>=0.24.0

## License

MIT License
iflow-mcp_epiphany-0312-anki-mcp 0.1.0
Add your description here
Requires Python: >=3.11

# genanki-mcp

Automated Anki deck generation service built with [genanki](https://github.com/kerrickstaley/genanki) and the [Model Context Protocol (MCP)](https://modelcontextprotocol.io/).  
Focuses on dependable deck creation, strict media sandboxing, and keeping the workflow lean—nothing more.

[中文文档](README.zh-CN.md)

## Highlights

- Streamable MCP HTTP tool that plugs into any MCP-compliant client
- Agents can craft rich CSS templates and embed media assets safely
- Temporary download links (default expiry 600 seconds) for `.apkg` retrieval
- Media path sandbox prevents out-of-scope file access and orphaned assets

## Project Layout

```
anki_mcp/
├── anki_tools/           # genanki utility wrapper
│   └── genanki_tool.py
├── server/               # MCP + HTTP entrypoints
│   ├── anki_models.py    # Pydantic request/response models
│   └── anki_server.py    # FastMCP / Starlette server
├── temp_anki_decks/      # Temporary .apkg exports
├── .envexample           # Environment variable template
├── pyproject.toml        # uv / pip project definition
└── uv.lock
```

## Quick Start

```bash
git clone https://github.com/Epiphany-0312/genanki-mcp.git
cd genanki-mcp
```

### Requirements

- Python ≥ 3.13
- Dependency management via [uv](https://github.com/astral-sh/uv) (recommended) or `pip`

### Install Dependencies

```bash
# With uv (recommended)
uv sync

# Or with pip
python -m venv .venv
.venv\Scripts\activate  # PowerShell
pip install -r requirements.txt
```

### Configure Environment Variables

Duplicate `.envexample` to `.env` and adjust as needed.

| Variable    | Description                                    | Default      |
| ----------- | ---------------------------------------------- | ------------ |
| `PORT`      | Uvicorn/FastMCP listening port                 | `10099`      |
| `SREVERIP`  | Public IP/host used to compose download links  | `127.0.0.1`  |
| `TOKEN`     | Optional bearer token protecting tool access   | (empty)      |

If `TOKEN` is omitted, the service skips authentication. Set it in production environments.

## Run the Service

```bash
uv run python -m server.anki_server
# or
python -m server.anki_server
```

FastMCP exposes the tool at `http://0.0.0.0:<PORT>/anki` and temporary downloads at `http://0.0.0.0:<PORT>/downloads`.

## Usage

Connect with any MCP-capable client. By default the MCP endpoint listens on `http://127.0.0.1:10099/anki`.

Cherry Studio example configuration and workflow:

![Cherry Studio configuration](./img/image.png)

![Cherry Studio tool list](./img/image-1.png)

![Cherry Studio deck preview](./img/image-2.png)

## License

MIT License
LAgencia-orion 1.0.32
Add your description here
Requires Python: >=3.11

publicar  el modulo en pypi
instalar el modulo
https://chatgpt.com/c/68e3e04f-be3c-8328-86a5-db6c221772474
https://chatgpt.com/c/68e3e04f-be3c-8328-86a5-db6c22177247


crear ejecutables con invoke para /scripts
https://chatgpt.com/c/68e3e37c-f180-832f-9ccb-1542f3c6aa1d



notas sqlalchemy
https://chatgpt.com/c/68dc236f-6b28-832e-943c-7d041e3c91c9



# =====================
https://chatgpt.com/c/68e55ff6-72c4-8332-b31a-16557791346a

# Contruir el paquete
uv build

uv pip install .

Si quieres editable (desarrollo):
uv pip install -e .


uv pip install ..\pr_install_pack\dist\pr_install_pack-0.1.0-py3-none-any.whl
uv pip install ..\pr_install_pack\dist\*.whl --force-reinstall


# con uv pip
Si lo instalaste desde wheel (.whl)
uv build
uv pip install -e ..\pr_install_pack
uv pip install ..\pr_install_pack\dist\pr_install_pack-0.1.0-py3-none-any.whl
uv pip uninstall ..\pr_install_pack\dist\pr_install_pack-0.1.0-py3-none-any.whl


# solo con uv
uv add ..\pr_install_pack\dist\pr_install_pack-0.1.0-py3-none-any.whl
uv add --editable ..\pr_install_pack
uv remove pr_install_pack


uv add C:\Users\Pc\Desktop\proyectos\orion\dist\orion-0.1.0-py3-none-any.whl

Remove-Item -Recurse -Force dist, build
uv build
uv publish --token TOKEN_PYPI
isage-benchmark-agent 0.1.0.1
SAGE Benchmark Agent - Agent capability evaluation framework
Requires Python: ==3.11.*
Keywords: sage, benchmark, agent, tool-selection, planning, evaluation, intellistream

# SAGE Benchmark Agent

Configuration-driven experiment framework for evaluating agent capabilities.

## Features

- **Tool Selection Evaluation**: Tool retrieval and ranking benchmarks
- **Planning Evaluation**: Multi-step planning with tool composition
- **Timing Detection**: Timing judgment for tool invocation decisions

## Quick Start

```bash
# Install
pip install isage-benchmark-agent

# Run tool selection experiment
sage-agent-bench tool-selection --config config/tool_selection_exp.yaml

# Run planning experiment
sage-agent-bench planning --config config/planning_exp.yaml
```

## Documentation

See [benchmark_agent/README.md](src/sage/benchmark/benchmark_agent/README.md) for detailed documentation.

## Development

```bash
# Clone
git clone https://github.com/intellistream/sage-agent-benchmark.git
cd sage-agent-benchmark

# Setup virtual environment
python -m venv .venv
source .venv/bin/activate  # On Windows: .venv\Scripts\activate

# Install in development mode
pip install -e ".[dev]"

# Run tests
pytest
```

## License

MIT License - see [LICENSE](LICENSE) for details.
torch_to_nnef 0.20.4
Any PyTorch Model to NNEF file format.
Keywords: ai, deep learning, exchange format, export, llm, machine learning, nnef, pytorch, torch, transformers

<!-- markdownlint-disable-file MD001 MD013 MD014 MD024 -->
# ![Torch to NNEF](./docs/img/torch_to_nnef.png)

[![dev workflow](https://github.com/sonos/torch-to-nnef/actions/workflows/dev.yml/badge.svg?branch=main)](https://github.com/sonos/torch-to-nnef/actions/workflows/dev.yml) ![python](https://img.shields.io/badge/python-%3E=3.9-green)[![documentation](https://img.shields.io/badge/torch_to_nnef-documentation-blue)]( https://sonos.github.io/torch-to-nnef/) ![MIT/Apache 2](https://img.shields.io/badge/license-MIT_OR_Apache_2.0-blue)

Export PyTorch models in a way that is auditable, debuggable, and stable across runtimes

> [!IMPORTANT]
> If you encounter any bug please follow [Bug report](https://sonos.github.io/torch-to-nnef/latest/contributing/guidelines/)  section instructions

## Goals & Scope

Torch-to-NNEF converts PyTorch models and tensors into NNEF, a simple and explicit neural network exchange format designed for reliable inference and long-term maintainability.

## Documentation

All documentation is available here:

[![documentation](https://img.shields.io/badge/torch_to_nnef-documentation-blue)]( https://sonos.github.io/torch-to-nnef/)
sstcam-tio 2.0.1
Pure Python reader of SST camera waveform files.
Requires Python: >=3.10

# <img align="right" src="https://gitlab.cta-observatory.org/cta-array-elements/sst/camera/analysis/tio/-/raw/main/misc/logo.jpg?ref_type=heads" height="200"></a>

# tio

[![pipeline status](https://gitlab.cta-observatory.org/cta-array-elements/sst/camera/analysis/tio/badges/master/pipeline.svg)](https://gitlab.cta-observatory.org/cta-array-elements/sst/camera/analysis/tio) [![coverage report](https://gitlab.cta-observatory.org/cta-array-elements/sst/camera/analysis/tio/badges/master/coverage.svg)](https://gitlab.cta-observatory.org/cta-array-elements/sst/camera/analysis/tio)

Python Reader for sstcam-waveform/TargetIO waveform files, utilising [fitsio](https://pypi.org/project/fitsio/) and [numba](https://numba.pydata.org/).

These files are FITS containing binary tables, which have been used as an
intermediate format to store waveforms from the SST (Small-Sized Telescope)
camera of CTAO (Cherenkov Telescope Array Observatory), and are generated
by the [sstcam-waveform](https://gitlab.cta-observatory.org/cta-array-elements/sst/camera/server/sstcam-server)
or the [TargetIO](https://gitlab.cta-observatory.org/cta-array-elements/common/targetlibraries/targetio) packages.

This package provides convenient read-only access to the waveform files, with support for both
R0 (uncalibrated) and R1 (calibrated) data, as full-camera and single-module files.
The reader is designed for efficient access to waveform data and event properties,
with support for iterating through events in a file.

* Supported Python versions: 3.10+
* Supported platforms: Linux, OSX
* Source: <https://gitlab.cta-observatory.org/cta-array-elements/sst/camera/analysis/tio>
* License: [BSD-3-Clause](LICENSE)

## Installation

This package is installable via pip from PyPI:
```
pip install sstcam-tio
```

This package is also installable via pip directly from this repository (via https or ssh), e.g.:
```
pip install 'git+https://gitlab.cta-observatory.org/cta-array-elements/sst/camera/analysis/tio.git'
```

## Optional Dependencies

Utilisation of Intel's short vector math library (SVML) for improved performance provided by numba:
```
conda install intel-cmplr-lib-rt
```

## Usage

```python
from tio import Reader
import numpy as np

path = "path/to/file_r0.tio"
event_index = 2
with Reader(path) as reader:

    # Read a single event
    event = reader[event_index]
    wfs = event.waveforms  # Access waveforms
    print(wfs.shape)  # Shape: (n_pixel, n_samples)
    first_cell_id = event.first_cell_id  # Access event properties

    # Iterate through file
    for event in reader:
        pass
```
iflow-mcp_msr2903-mercari-mcp 0.1.2
A FastMCP server to search Mercari Japan.
Requires Python: >=3.10

## Demo
<img src="/assets/demo.gif" width="600" height="600"/>

## Requirements

- Python 3.11 or higher
- Dependencies as listed in `pyproject.toml`, including:
  - mcp
  - mercari
  - pydantic
    
## Pre-setup

1. uv
   https://docs.astral.sh/uv/getting-started/installation/

2. Microsoft C++ Build Tools (For Windows)
   https://visualstudio.microsoft.com/visual-cpp-build-tools/

## Setup

1. Clone this repository:
   ```bash
   git clone https://github.com/msr2903/mercari-jp-mcp.git
   cd mercari-jp-mcp
   ```

2. Create and activate a virtual environment and install dependencies:
   ```bash
   uv venv
   source .venv/bin/activate  # On Windows: .venv\Scripts\activate
   uv pip install -e .
   ```

## Usage

### Development Mode

You can test the server with MCP Inspector by running:

```bash
uv run server.py
```

You can also test to input manually the query, exclude keywords, min and max price by running:

```bash
uv run check_server.py
```

This will start the server and allow you to test the available tools.

### Integration with Claude for Desktop

To integrate this server with Claude for Desktop:

1. Install Claude for Desktop to your local machine.
2. Install VS Code to your local machine. Then run the following command to open the `claude_desktop_config.json` file:
   - MacOS: `code ~/Library/Application\ Support/Claude/claude_desktop_config.json`
   - Windows: `code $env:AppData\Claude\claude_desktop_config.json`

3. Edit the Claude for Desktop config file, located at:
   - macOS: 
     ```json
     {
       "mcpServers": {
         "mercari": {
           "command": "uv",
           "args": [
             "--directory",
             "/ABSOLUTE/PATH/TO/PARENT/FOLDER/mercari-jp-mcp",
             "run",
             "server.py"
           ]
         }
       }
     }
     ```
   - Windows:
     ```json
     {
       "mcpServers": {
         "mercari": {
           "command": "uv",
           "args": [
             "--directory",
             "C:\\ABSOLUTE\\PATH\\TO\\PARENT\\FOLDER\\mercari-jp-mcp",
             "run",
             "server.py"
           ]
         }
       }
     }
     ```

   - **Note**: You may need to put the full path to the uv executable in the command field. You can get this by running `which uv` on MacOS/Linux or `where uv` on Windows.

4. Restart Claude for Desktop

## Thanks
This work would not have been possible without amazing open source projects, including (but not limited to):

- jlowin/fastmcp (https://github.com/jlowin/fastmcp)
- marvinody/mercari (https://github.com/marvinody/mercari/)

Thank you to the authors of these projects for making them available to the community!