> ## Documentation Index
> Fetch the complete documentation index at: https://praison.ai/docs/llms.txt
> Use this file to discover all available pages before exploring further.

# Ollama Python MCP Integration

> Guide for integrating Ollama models with Python MCP servers using PraisonAI agents

## Add Ollama Python Tool to AI Agent

```mermaid theme={"theme":{"light":"vitesse-light","dark":"vitesse-dark"}}
flowchart LR
    In[In] --> Agent[AI Agent]
    Agent --> Tool[Stock Price MCP]
    Tool --> Agent
    Agent --> Out[Out]
    
    style In fill:#8B0000,color:#fff
    style Agent fill:#2E8B57,color:#fff
    style Tool fill:#3776AB,color:#fff
    style Out fill:#8B0000,color:#fff
```

## Quick Start

<Steps>
  <Step title="Set Up Ollama">
    Make sure you have Ollama installed and running locally:

    ```bash theme={"theme":{"light":"vitesse-light","dark":"vitesse-dark"}}
    # Install Ollama from https://ollama.ai/
    # Pull the llama3.2 model
    ollama pull llama3.2
    ```
  </Step>

  <Step title="Create a file">
    Create a new file `ollama_stock.py` with the following code:

    ```python theme={"theme":{"light":"vitesse-light","dark":"vitesse-dark"}}
    from praisonaiagents import Agent, MCP

    stock_agent = Agent(
        instructions="""You are a Stock Price Assistant.""",
        llm="ollama/llama3.2",
        tools=MCP("/path/to/your/venv/bin/python /path/to/your/app.py")
    )

    # NOTE: Python Path replace with yours: /path/to/your/venv/bin/python
    # NOTE: app.py file path, replace it with yours: /path/to/your/app.py

    stock_agent.start("What is the Stock Price of Apple?")
    ```
  </Step>

  <Step title="Install Dependencies">
    Make sure you have the required packages installed:

    ```bash theme={"theme":{"light":"vitesse-light","dark":"vitesse-dark"}}
    pip install "praisonaiagents[llm]" yfinance mcp
    ```
  </Step>

  <Step title="Run the Agent">
    Execute your script:

    ```bash theme={"theme":{"light":"vitesse-light","dark":"vitesse-dark"}}
    python ollama_stock.py
    ```
  </Step>
</Steps>

<Note>
  **Requirements**

  * Python 3.10 or higher
  * Ollama installed and running locally
  * yfinance and mcp packages installed
</Note>

## Features

<CardGroup cols={2}>
  <Card title="Local LLM" icon="server">
    Run models locally using Ollama without relying on external APIs.
  </Card>

  <Card title="Stock Data" icon="chart-line">
    Retrieve real-time stock price information using the yfinance library.
  </Card>

  <Card title="Python MCP" icon="python">
    Use Python-based MCP servers for custom tool functionality.
  </Card>

  <Card title="Privacy-Focused" icon="shield-alt">
    Keep sensitive data local with on-device inference.
  </Card>
</CardGroup>