Agentic AI

The command line is the backbone of productivity for many developers, data professionals, and content creators. Yet with growing volumes of files, drafts, and project assets, even the most organized users face content chaos — duplicate files, forgotten revisions, and manual cleanup that eats into creative time.

Enter Gemini CLI: an open-source AI agent that brings Google Gemini’s natural language intelligence right to your terminal. Gemini CLI isn’t just about coding — it’s a smarter way to search, summarize, compare, organize, and automate file and content workflows using plain English.

What Makes Gemini CLI Stand Out?

•  Direct Access to Gemini 2.5 Pro:
   Instant, lightweight access to a powerful AI model with a generous token context window — ideal for large files and lengthy content.
•  Natural Language Commands:
   Forget obscure flags or complex scripts. Just ask in everyday language, and Gemini CLI understands your intent.
•  Productivity Beyond Coding:
   Whether you’re sorting research notes, summarizing docs, or managing creative assets, Gemini CLI adapts to your workflow.

Practical Ways Gemini CLI Boosts Productivity

Here are real-world scenarios where Gemini CLI shines:

• 🔍 Find and Remove Duplicate Files:
   gemini "Scan this folder for duplicate PDFs and list them"
 gemini "Find images with similar names and flag potential duplicates"
• 📝 Summarize Key Content:
   gemini "Summarize the main points from all meeting notes in this directory"
 gemini "Extract key differences between Draft_v1.docx and Draft_v2.docx"
• 🗂️ Organize and Rename Files:
   gemini "Organize all documents by project and year"
 gemini "Batch rename files in this folder using a consistent naming scheme"
• 🔎 Search by Natural Language:
   gemini "Show all presentations from 2024 with more than 10 slides"
 gemini "List files modified in the last 7 days containing the word ‘proposal’"
• ⚙️ Automate Repetitive Actions:
   gemini "Move all .txt files older than 6 months to the archive folder"
 gemini "Delete temporary files with 'backup' in their names after review"
• 🛠️ Content Generation and Debugging:
   gemini "Draft a README.md based on the contents of this project folder"
 gemini "Review this Python script and suggest improvements"

My Experiment: Gemini CLI in Action

To see Gemini CLI in action, I pointed it at one of my project folders — a mix of presentations, reports, and working drafts. With just a few natural language commands, Gemini CLI quickly analyzed the folder, flagged duplicate files, outlined unique documents, and delivered a clear, actionable summary. What would have taken much longer to sort manually was resolved in minutes.

I also tried a creative utility: asking Gemini CLI to take a screenshot of my screen and convert it to JPG. The tool prompted me for the necessary permissions and guided me to grant Terminal access on my Mac. Once enabled, Gemini CLI handled the task seamlessly — showcasing how agent-powered CLI can integrate real-world utility features right into your workflow.

Download the Gemini CLI at – https://blog.google/technology/developers/introducing-gemini-cli-open-source-ai-agent/

Important Caveats and Best Practices

• File Access and Permissions:
   Gemini CLI can access and modify your files. Always check which folders you’re targeting, especially with move or delete commands.
• Accidental Deletion:
   AI-powered deletion is fast but irreversible. Add confirmation prompts or use a “dry run” before destructive commands.
• Sensitive Content:
   Avoid processing sensitive files unless you’re clear on how data is handled locally vs. in the cloud (refer to documentation).
• Versioning and Auditability:
   For important assets, enable file versioning or keep a changelog to track changes made via Gemini CLI.
• AI Limitations:
  Review AI suggestions, especially for bulk operations. Natural language is powerful — but not perfect.

Final Thoughts: The Future Is Agentic

Gemini CLI brings much-needed order and intelligence to content management in the terminal. By combining natural language with robust AI, it transforms how we interact with files, automate tasks, and create content. For developers, creators, and knowledge workers, it’s a way to reclaim time and reduce manual overhead — when used thoughtfully.

💡 This is just one example of how an integrated agent CLI can make a difference. Looking ahead, it’s clear that future operating systems will be powered by smart agents — completely changing how we interact with files, applications, and information across our digital lives.

Modern financial analysis is rapidly moving toward automation and agentic workflows. Integrating large language models (LLMs) with real-time financial data unlocks not just powerful insights but also entirely new ways of interacting with portfolio data.

This post walks through a practical, autonomous solution using Google ADK, Zerodha’s Kite MCP protocol, and an LLM for actionable portfolio analytics. The full workflow and code are available on GitHub.

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Why This Stack?

• Google ADK: Enables LLM agents to interact with live tools, APIs, and event streams in a repeatable, testable way.
• Zerodha MCP (Model Control Protocol): Provides a secure, real-time API to portfolio holdings using Server-Sent Events (SSE).
• LLMs (Gemini/GPT-4o): Analyze portfolio data, highlight concentration risk, and offer actionable recommendations.

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Architecture Overview

The workflow has three main steps:

1. User authenticates with Zerodha using an OAuth browser flow.
2. The agent retrieves live holdings via the MCP get_holdings tool.
3. The LLM agent analyzes the raw data for risk and performance insights.

All API keys and connection details are managed through environment variables for security and reproducibility.

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Key Code Snippets

1. Environment and Dependency Setup

import os
from dotenv import load_dotenv

# Load API keys and config from .env
load_dotenv('.env')
os.environ["GOOGLE_API_KEY"] = os.environ["GOOGLE_API_KEY"]
os.environ["GOOGLE_GENAI_USE_VERTEXAI"] = "False"

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2. ADK Agent and Toolset Initialization

from google.adk.agents.llm_agent import LlmAgent
from google.adk.tools.mcp_tool.mcp_toolset import MCPToolset, SseServerParams

MCP_SSE_URL = os.environ.get("MCP_SSE_URL", "https://mcp.kite.trade/sse")

toolset = MCPToolset(
    connection_params=SseServerParams(url=MCP_SSE_URL, headers={})
)

root_agent = LlmAgent(
    model='gemini-2.0-flash',
    name='zerodha_portfolio_assistant',
    instruction=(
        "You are an expert Zerodha portfolio assistant. "
        "Use the 'login' tool to authenticate, and the 'get_holdings' tool to fetch stock holdings. "
        "When given portfolio data, analyze for concentration risk and best/worst performers."
    ),
    tools=[toolset]
)

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3. Orchestrating the Workflow

from google.adk.sessions import InMemorySessionService
from google.adk.artifacts.in_memory_artifact_service import InMemoryArtifactService
from google.adk.runners import Runner
from google.genai import types

import asyncio

async def run_workflow():
    session_service = InMemorySessionService()
    artifacts_service = InMemoryArtifactService()
    session = await session_service.create_session(
        state={}, app_name='zerodha_portfolio_app', user_id='user1'
    )

    runner = Runner(
        app_name='zerodha_portfolio_app',
        agent=root_agent,
        artifact_service=artifacts_service,
        session_service=session_service,
    )

    # 1. Login Step
    login_query = "Authenticate and provide the login URL for Zerodha."
    content = types.Content(role='user', parts=[types.Part(text=login_query)])
    login_url = None
    async for event in runner.run_async(session_id=session.id, user_id=session.user_id, new_message=content):
        if event.is_final_response():
            import re
            match = re.search(r'(https?://[^\s)]+)', getattr(event.content.parts[0], "text", ""))
            if match:
                login_url = match.group(1)
    if not login_url:
        print("No login URL found. Exiting.")
        return
    print(f"Open this URL in your browser to authenticate:\n{login_url}")
    import webbrowser; webbrowser.open(login_url)
    input("Press Enter after completing login...")

    # 2. Fetch Holdings
    holdings_query = "Show my current stock holdings."
    content = types.Content(role='user', parts=[types.Part(text=holdings_query)])
    holdings_raw = None
    async for event in runner.run_async(session_id=session.id, user_id=session.user_id, new_message=content):
        if event.is_final_response():
            holdings_raw = getattr(event.content.parts[0], "text", None)
    if not holdings_raw:
        print("No holdings data found.")
        return

    # 3. Analysis
    analysis_prompt = f"""
You are a senior portfolio analyst.

Given only the raw stock holdings listed below, do not invent or assume any other holdings.

1. **Concentration Risk**: Identify if a significant percentage of the total portfolio is allocated to a single stock or sector. Quantify the largest exposures, explain why this matters, and suggest specific diversification improvements.

2. **Performance Standouts**: Clearly identify the best and worst performing stocks in the portfolio (by absolute and percentage P&L), and give actionable recommendations.

Raw holdings:

{holdings_raw}

Use only the provided data.
"""
    content = types.Content(role='user', parts=[types.Part(text=analysis_prompt)])
    async for event in runner.run_async(session_id=session.id, user_id=session.user_id, new_message=content):
        if event.is_final_response():
            print("\n=== Portfolio Analysis Report ===\n")
            print(getattr(event.content.parts[0], "text", ""))

asyncio.run(run_workflow())

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Security and Environment Configuration

All API keys and MCP endpoints are managed via environment variables or a .env file.
Never hardcode sensitive information in code.

Example .env file:

GOOGLE_API_KEY=your_google_gemini_api_key
MCP_SSE_URL=https://mcp.kite.trade/sse

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What This Enables

• Reproducible automation: Agents can authenticate, retrieve, and analyze portfolios with minimal human input.
• Extensibility: Easily add more tools (orders, margins, etc.) or more advanced analytic prompts.
• Separation of concerns: Business logic, security, and agent workflow are all clearly separated.

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Repository

Full working code and documentation:
https://github.com/navveenb/agentic-ai-worfklows/tree/main/google-adk-zerodha

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This workflow is for educational and portfolio analysis purposes only. Not investment advice.