Ksrk Mcp Server Client: Real-Time Sync & Custom Control

Ksrk Mcp Server Client: Grab real-time updates, sync with Claude Desktop, or build your own custom client – your data, your rules. Effortless control, hardcore flexibility.

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About Ksrk Mcp Server Client

What is Ksrk Mcp Server Client: Real-Time Sync & Custom Control?

It’s a Python-based tool designed for real-time interaction between a client and a MCP server. The client connects to the server to access custom tools like web scraping, URL fetching, and AI-driven query processing. Core features include synchronization with OpenAI’s GPT-4 and integration with ScrapingDog API for dynamic data retrieval.

How to use Ksrk Mcp Server Client: Real-Time Sync & Custom Control?

Follow these steps to get started:

Clone the repository and set up a virtual environment.
Install dependencies from pyproject.toml using pip.
Create a .env file with API keys for ScrapingDog and OpenAI.
Run python client.py to start the interactive prompt loop. Type your queries and exit with quit or exit.

Ksrk Mcp Server Client Features

Key Features of Ksrk Mcp Server Client: Real-Time Sync & Custom Control?

Real-time synchronization ensures seamless data exchange between client and server. The client supports:

Asynchronous tool calls for web scraping via ScrapingDog.
Integration with GPT-4 for natural language processing.
Custom MCP tools like about_ksrk for website-specific searches.
Test scripts to validate scraping workflows with httpx and BeautifulSoup.

Use cases of Ksrk Mcp Server Client: Real-Time Sync & Custom Control?

Use this tool to:

Automate real-time data extraction from websites.
Generate dynamic responses by combining AI and web data.
Build custom workflows with tools like URL content fetching or keyword-based site analysis.
Test scraping logic without deploying full production environments.

Ksrk Mcp Server Client FAQ

FAQ from Ksrk Mcp Server Client: Real-Time Sync & Custom Control?

Q: Does it require Python 3.13?
A: Yes, the project depends on features only available in Python 3.13.

Q: How do I handle dependencies?
A: Install via pip install -r requirements.txt after setting up the environment.

Q: What if my API keys are missing?
A: Add them to the .env file exactly as shown in the setup guide. Missing keys will block server access.

Q: Can I modify the tools?
A: Absolutely—edit ksrk-mcp-server.py or add new tools to the MCP framework.

Content

Requirements

Python 3.13
Dependencies listed in pyproject.toml

Installation

Clone the repository:

git clone

cd documentation

Create a virtual environment and activate it:

python -m venv .venv

source .venv/bin/activate  # On Windows use `.venv\Scripts\activate`

Install the dependencies:

pip install -r requirements.txt
Set up environment variables:

Create a .env file in the root directory with the following content:

    SCRAPING_DOG_API_KEY=your_scraping_dog_api_key
OPENAI_API_KEY=your_openai_api_key

Usage

Running the Client

Navigate to the root directory:

cd ..
Run the client:

python client.py
Enter your prompts in the interactive prompt loop. Type quit or exit to stop the client.

Project Files

`client.py`

This file contains the main client code that interacts with the MCP server and OpenAI's GPT-4 model. It includes the following key components:

MCPClient: A class that manages the connection to the MCP server and provides methods to retrieve available tools and call them.
agent_loop: An asynchronous function that processes user queries using the LLM and available tools.
main: The main function that sets up the MCP server, initializes tools, and runs the interactive loop.

`ksrk-mcp/ksrk-mcp-server.py`

This file contains the MCP server implementation. It includes the following key components:

search_web: An asynchronous function that searches the web using the ScrapingDog API.
fetch_url: An asynchronous function that fetches the content of a URL.
about_ksrk: An MCP tool that searches for details about "ksrk" on a given website.

`ksrk-mcp/test-website.py`

This file contains a script to test website scraping using httpx and BeautifulSoup.

License

This project is licensed under the MIT License. See the LICENSE file for details.

Acknowledgements

OpenAI for providing the GPT-4 model.
ScrapingDog for the web scraping API.
BeautifulSoup for parsing HTML and XML documents.
httpx for the HTTP client.