This section was written completely by using the SentinelCore agent and its tools(prompt->get the details from internet->write it to a file) via gemini 2.0 flash.
About
SentinelCore is an advanced AI agent powered by Model Context Protocol. It can browse the web, interact with local file systems, and is designed to keep evolving with new features. Whether you're looking for a smart assistant, a system manager, or a knowledge guide, SentinelCore
Details
- Author
- bhuvanmdev
- GitHub stars
- 1
- Downloads
- 328
- Categories
- AI, Automation
Jump to
- File existence checking and read/write operations (text and binary).
- Current date and time retrieval.
- AI-powered web search via Brave Search agent.
- Web page scraping to markdown with optional vector indexing.
- Vector index management: list all indexes and search via an embedding model.
Configure the server by setting environment variables (including an LLM API key) and a JSON server configuration file. The server is started using mcp.run(transport="stdio"). The included client module (client.py) connects to the server, lists available tools, and runs a chat session where the LLM can invoke tools to answer user queries.
This section was written completely by using the SentinelCore agent and its tools(prompt->get the details from internet->write it to a file) via gemini 2.0 flash.
USER-PROMPT:-Now first search for a github account named bhuvanmdev and scrape his fontpage and search for a repo that has something to do with MCP application. Then go to that repository and and scrape the client.py and server.py file contents and create a neat summary of it and write it to readme.md file in the current dir.
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