semvec/pypi-packages huggingface dataset:
iflow-mcp_rahul007-bit-mcp-k8s 0.1.0
Kubernetes assistant powered by Model Context Protocol (MCP)
Requires Python: >=3.10

# K8s MCP

A Kubernetes assistant powered by Model Context Protocol (MCP) and Google Gemini.

## Requirements

- Python 3.10+
- Kubernetes cluster with kubeconfig configured
- Node.js 18+ (for frontend)
- Bun (optional, for faster frontend builds)

## Quick Start

Use the Makefile to run different components:

### 1. MCP Server

Starts the MCP server that communicates with your Kubernetes cluster.

```bash
make mcp-server
```

Requires: Active Kubernetes cluster and configured kubeconfig.

### 2. CLI Mode

Interactive command-line interface to query your Kubernetes cluster.

```bash
make cli
```

### 3. Web Application (Backend + Frontend)

Start the backend API server:

```bash
make backend
```

Start the frontend in another terminal:

```bash
make frontend
```

Then open http://localhost:5173 in your browser.

### 4. All at Once

Start all services in background (development mode):

```bash
make dev
```

## Project Structure

```
k8s_mcp_server.py    - MCP server implementation
mcp_client.py        - CLI client
app/
  backend/           - FastAPI server
  frontend/          - React UI
```

## Configuration

Ensure your kubeconfig is at `~/.kube/config` or set the `KUBECONFIG` environment variable.

The MCP server exposes 32 Kubernetes tools for managing resources, deployments, services, and more.

## Features

- Real-time Kubernetes operations via MCP tools
- Chat interface with tool call results
- Inline tool call display in messages
- WebSocket streaming for live responses
- Automatic WebSocket reconnection

### Make Commands

Available make targets (run `make <target>`):

- `make help` — Show all available commands  
- `make mcp-server` — Start the K8s MCP server  
- `make cli` — Launch the interactive CLI client  
- `make backend` — Start the FastAPI backend  
- `make frontend` — Start the React frontend  
- `make dev` — Start backend and frontend in the background (development mode)  
- `make logs` — Tail service logs  
- `make stop` — Stop all background services  
- `make install-deps` — Install project dependencies  
- `make clean` — Remove build artifacts and cache files
iflow-mcp_ImVirtue-RagChatbot_MCPServer 0.1.0
A RAG-based HR chatbot with MCP server for document retrieval and email functionality
Requires Python: >=3.11

# Rag chatbot with a localhost MCP server
Building a RAG-based HR chatbot for providing rules in the workplace with the **localhost MCP server** as a function-calling hub
<img src="images/rag.jpg" width="500">
<img src="images/mcp-architecture.png" width="500">

## Overview
This project implements a Retrieval-Augmented Generation (RAG) chatbot using Streamlit and the MCP server. Users can upload PDF files, and the chatbot retrieves relevant information from the PDFs to answer natural language questions. The system leverages OpenAI models, LangChain utilities, and an in-memory vector store for efficient document retrieval.

## Features
- **MCP Tool Integration**:  
  Tool orchestration with **MCP** ensures smooth communication between document indexing, retrieval, and answer generation. The     backend tools can be extended or replaced easily as new functionalities are added.
  
- **PDF Upload and Parsing**:  
  Upload a PDF file which is then parsed using `PDFPlumberLoader` to extract text content.

- **Text Chunking**:  
  The extracted text is split into smaller chunks using `RecursiveCharacterTextSplitter` to facilitate efficient indexing and retrieval.

- **Document Indexing**:  
  Chunks are indexed in an in-memory vector store with embeddings generated via `OpenAIEmbeddings`.

- **Similarity Search (Consine Similarity)**:  
  When a user submits a query, the chatbot performs a similarity search to retrieve the most relevant document chunks based on the query.

- **Prompt-Based Answer Generation**:  
  A custom prompt template combines the user question with retrieved context, and a GPT-4 powered LLM (using `ChatOpenAI`) generates the final answer.

- **Interactive Interface**:  
  The application uses Streamlit to provide an interactive, chat-like interface where user questions and bot responses are displayed.

## Result
<img src="images/HRwithMCP.png" width="700">
readunwise 0.1.6
CLI for managing Amazon Kindle highlights

# readunwise

CLI for managing Amazon Kindle highlights and a simple alternative to [Readwise](https://readwise.io/)

```sh
pip install readunwise
```

| Command  | Description                                 |
| -------- | ------------------------------------------- |
| ls       | List clippings file                         |
| cat      | Display highlights from a book              |
| diff     | Compare clippings files                     |
| save     | Save clippings file                         |
| random   | Print a random highlight                    |
| discord  | Send random highlights to a Discord channel |
| readwise | Display recent highlights from Readwise API |

![Example](example.png)