semvec/pypi-packages huggingface dataset:
mcp-server-tree-sitter-extra 0.0.1
MCP Server for Tree-sitter code analysis
License: MIT
Requires Python: >=3.10

# MCP Tree-sitter Server (Extended)

An extension over https://github.com/wrale/mcp-server-tree-sitter

## Features

- Extra language mappings by default
- Compact AST tree (table-of-content)

## Installation

### Prerequisites

- Python 3.10+
- Tree-sitter language parsers for your preferred languages

### Basic Installation

```bash
pip install mcp-server-tree-sitter-extra
```

## Quick Start

### Setting up with AIRun

1. Open your server.json configuration file:

2. Add the server to the `mcpServers` section:

   ```json
   "tree_sitter": {
      "command": "uvx",
      "args": [
        "--index-url",
        "https://nexus-ci.core.kuberocketci.io/repository/krci-python-group/simple/",
        "--from",
        "mcp-server-tree-sitter-extra",
        "mcp-server-tree-sitter-extra"
      ]
    }
   ```

## License

MIT
mcp-json-to-csv 0.0.1
MCP Server for JSON to CSV convertion.
License: MIT
Requires Python: >=3.10
Keywords: convertion, csv, json, mcp

# JSON to CSV Converter

---

A Model Context Protocol (MCP) server implementation for a simple tool that converts JSON arrays of objects to CSV format.

![License](https://img.shields.io/badge/license-MIT-blue.svg)
![Python Version](https://img.shields.io/badge/python-3.10%2B-blue)
![MCP Protocol](https://img.shields.io/badge/MCP-Compatible-green)

---

# Table of Contents

1. [Description](#description)
2. [Usage](#usage)
3. [Avaliable Tools](#available-tools)
    - [json-to-csv](#json-to-csv)
9. [License](#license)

---

## Description

This tool provides a service that takes a JSON string containing an array of objects (dictionaries) and converts it to a CSV string. The first object's keys are used as the CSV header.

## Usage

This tool is designed to work as an MCP (Model Control Protocol) server. You can configure it in your project:

```json
{
    "mcpServers": {
        "json-to-csv": {
            "command": "uvx",
            "args": [
                "--index-url",
                "python_package_index_url",
                "--from",
                "mcp-json-to-csv",
                "json-to-csv"
                
            ]
        }
    }
}
```

## Available Tools

### `json-to-csv`

Converts a JSON array of objects to CSV format.

**Input Schema:**

```json
{
    "json_input": "JSON string to convert"
}
```

**Example Input:**

```json
{
    "json_input": "[{\"name\":\"John\",\"age\":30},{\"name\":\"Jane\",\"age\":25}]"
}
```

**Example Output:**

```csv
name,age
John,30
Jane,25
```

## Requirements

- Python 3.10+
- uv (https://docs.astral.sh/uv/getting-started/installation/)

## License

This project is licensed under the MIT License - see the [LICENSE](LICENSE) file for details.

 ---

For more information or support, please open an issue on the project repository.
cloud-data-cockpit 1.0.4
An IPython widget for browsing, benchmarking, and processing S3 datasets.
License: MIT
Requires Python: >=3.10

# Data Cockpit

**Data Cockpit** is an interactive IPython widget built on top of the [Dataplug](https://github.com/dataplug/dataplug) framework. It enables scientists and engineers to:

- **Upload** and browse datasets in Amazon S3  
- **Explore** curated public and Metaspace collections  
- **Benchmark** performance to discover optimal batch sizes  
- **Partition** a variety of scientific data types into chunks or batches  
- **Integrate** seamlessly into Jupyter notebooks for elastic, parallel workloads  

---

## Why Data Cockpit?

### Built on Dataplug’s Cloud-Aware Partitioning

Dataplug is a client-side Python framework for **dynamic, zero-cost data slicing** of unstructured scientific data stored in object stores like S3. It:

- **Pre-processes** data in a read-only fashion, building lightweight indexes decoupled from the raw objects  
- **Exploits** S3 byte-range reads to parallelize high-bandwidth access across many workers  
- **Supports** a plug-in interface for multiple domains:  
  - **Generic**: CSV, raw text  
  - **Genomics**: FASTA, FASTQ, VCF  
  - **Geospatial**: LiDAR, Cloud-Optimized Point Cloud (COPC), COG  
  - **Metabolomics**: ImzML  
- **Allows** re-partitioning with different strategies without rewriting the original data  

### What Data Cockpit Adds

While Dataplug focuses on efficient data slicing, Data Cockpit provides an **end-to-end Jupyter UI** that:

1. **Uploads** your local files directly into any S3 bucket  
2. **Browses** existing buckets or public datasets from the AWS Open Data Registry  
3. **Runs benchmarks** across a configurable range of batch sizes to find the fastest throughput  
4. **Processes & partitions** your data with one click, displaying progress and results entirely in-notebook  
5. **Retrieves** partitions via `get_data_slices()`, which returns the DataPlug data slices (metadata) for downstream processing  

---

## Installation

```bash
pip install cloud-data-cockpit
```

Or install both Data Cockpit and geospatial extras together:

```bash
pip install cloud-data-cockpit[geospatial]  
```