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This section was written completely by using the SentinelCore ag…

@bhuvanmdev

Repository

Basic information

Category

AI & Agents

Transports

stdio

Author

bhuvanmdev

About 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. MCP Server

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

#automation#agentic-ai#mcp-server#mcp-client

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. MCP Server config

No standard config provided

This server doesn't expose a parseable MCP config block in its README. See the repository for install instructions.

Repository

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. MCP Server tools

Tool list not available yet

This listing does not currently have a tool list to display. This does not mean the server has no tools; check its documentation for supported capabilities.

We auto-extract tools from the README. The maintainer can list them under a ## Tools heading to populate this section.

Overview

What is SentinelCore Agent?

SentinelCore Agent is an MCP (Model Context Protocol) server that provides tools for file system operations, web scraping, AI-powered web search, and vector-based indexing. It is designed to be used with an LLM client that orchestrates tool calls, supporting both Azure OpenAI and Google Gemini models.

How to use SentinelCore Agent?

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.

Key features of SentinelCore Agent

  • 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.

Use cases of SentinelCore Agent

  • Automate file reading, writing, and metadata checks on a local filesystem.
  • Scrape web pages and store their content as vector indexes for later retrieval.
  • Power a conversational AI agent that can search the web and manipulate files based on natural language prompts.
  • Build a research assistant that scrapes pages, indexes them, and answers queries using semantic search.

Frequently asked questions

1

What transports and authentication does the server support?

The server uses `transport="stdio"` and does not mention built-in authentication.

2

What are the main dependencies?

The server depends on `fastmcp`, `crawl4ai` (asynchronous web crawler), and an external embedding model for vector operations.

3

How does the client communicate with the LLM?

The client supports Azure OpenAI and Google Gemini models, configuring them via environment variables and managing tool call execution with retry logic.

4

Where are vector indexes stored?

Vector indexes are stored as local files in the current working directory.

5

Does the server have any known limitations?

The README does not list any specific limitations or rate limits.

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