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Merge pull request #433 from MervinPraison/develop
Update navigation and MCP integration
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‎docs/mcp/airbnb.mdx‎

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---
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title: "Airbnb MCP Integration"
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sidebarTitle: "Airbnb"
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description: "Guide for integrating Airbnb booking capabilities with PraisonAI agents using MCP"
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icon: "airbnb"
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---
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# Airbnb MCP Integration
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```mermaid
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flowchart LR
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In[In] --> Agent[AI Agent]
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Agent --> Tool[Airbnb MCP]
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Tool --> Agent
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Agent --> Out[Out]
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style In fill:#8B0000,color:#fff
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style Agent fill:#2E8B57,color:#fff
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style Tool fill:#FF5A5F,color:#fff
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style Out fill:#8B0000,color:#fff
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```
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## Quick Start
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<Steps>
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<Step title="Set API Key">
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Set your OpenAI API key as an environment variable in your terminal:
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```bash
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export OPENAI_API_KEY=your_openai_api_key_here
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```
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</Step>
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<Step title="Create a file">
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Create a new file `airbnb_search.py` with the following code:
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```python
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from praisonaiagents import Agent, MCP
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search_agent = Agent(
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instructions="""You help book apartments on Airbnb.""",
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llm="gpt-4o-mini",
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tools=MCP("npx -y @openbnb/mcp-server-airbnb --ignore-robots-txt")
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)
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search_agent.start("I want to book an apartment in Paris for 2 nights. 03/28 - 03/30 for 2 adults")
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```
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</Step>
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<Step title="Install Dependencies">
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Make sure you have Node.js installed, as the MCP server requires it:
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```bash
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pip install praisonaiagents
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```
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</Step>
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<Step title="Run the Agent">
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Execute your script:
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```bash
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python airbnb_search.py
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```
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</Step>
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</Steps>
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<Note>
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**Requirements**
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- Python 3.10 or higher
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- Node.js installed on your system
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- OpenAI API key (for the agent's LLM)
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</Note>
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## Features
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<CardGroup cols={2}>
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<Card title="Accommodation Search" icon="hotel">
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Search for accommodations on Airbnb with natural language queries.
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</Card>
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<Card title="MCP Integration" icon="plug">
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Seamless integration with Model Context Protocol.
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</Card>
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<Card title="Booking Details" icon="calendar">
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Specify dates, guests, and location preferences in natural language.
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</Card>
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<Card title="NPM Package" icon="js">
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Leverages the official Airbnb MCP server package.
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</Card>
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</CardGroup>

‎docs/mcp/bravesearch.mdx‎

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---
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title: "Brave Search MCP Integration"
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sidebarTitle: "Brave Search"
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description: "Guide for integrating Brave Search capabilities with PraisonAI agents using MCP"
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icon: "searchengin"
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---
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# Brave Search MCP Integration
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```mermaid
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flowchart LR
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In[In] --> Agent[AI Agent]
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Agent --> Tool[Brave Search MCP]
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Tool --> Agent
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Agent --> Out[Out]
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style In fill:#8B0000,color:#fff
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style Agent fill:#2E8B57,color:#fff
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style Tool fill:#4169E1,color:#fff
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style Out fill:#8B0000,color:#fff
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```
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## Quick Start
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<Steps>
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<Step title="Set API Key">
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Set your Brave Search API key as an environment variable in your terminal:
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```bash
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export BRAVE_API_KEY=your_brave_api_key_here
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export OPENAI_API_KEY=your_openai_api_key_here
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```
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You can obtain a Brave Search API key from [Brave Search API](https://brave.com/search/api/).
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</Step>
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<Step title="Create a file">
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Create a new file `brave_search.py` with the following code:
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```python
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from praisonaiagents import Agent, MCP
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import os
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# Use the API key from environment or set it directly
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brave_api_key = os.getenv("BRAVE_API_KEY") or "your_brave_api_key_here"
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# Use a single string command with environment variables
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search_agent = Agent(
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instructions="""You are a helpful assistant that can search the web for information.
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Use the available tools when relevant to answer user questions.""",
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llm="gpt-4o-mini",
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tools=MCP("npx -y @modelcontextprotocol/server-brave-search", env={"BRAVE_API_KEY": brave_api_key})
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)
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search_agent.start("Search more information about AI News")
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```
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</Step>
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<Step title="Install Dependencies">
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Make sure you have Node.js installed, as the MCP server requires it:
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```bash
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pip install praisonaiagents
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```
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</Step>
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<Step title="Run the Agent">
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Execute your script:
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```bash
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python brave_search.py
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```
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</Step>
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</Steps>
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<Note>
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**Requirements**
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- Python 3.10 or higher
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- Node.js installed on your system
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- Brave Search API key
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- OpenAI API key (for the agent's LLM)
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</Note>
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## Features
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<CardGroup cols={2}>
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<Card title="Web Search" icon="globe">
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Search the web for up-to-date information.
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</Card>
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<Card title="MCP Integration" icon="plug">
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Seamless integration with Model Context Protocol.
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</Card>
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<Card title="Environment Variables" icon="key">
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Secure API key handling through environment variables.
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</Card>
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<Card title="NPM Package" icon="js">
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Leverages the official Brave Search MCP server package.
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</Card>
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</CardGroup>

‎docs/mcp/custom.mdx‎

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---
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title: "Custom Python MCP Server"
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sidebarTitle: "Custom Python MCP"
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description: "Guide for creating and using custom Python MCP servers with PraisonAI agents"
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icon: "python"
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---
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# Custom Python MCP Server
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```mermaid
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flowchart LR
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In[In] --> Agent[AI Agent]
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Agent --> Tool[Custom Python MCP]
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Tool --> Agent
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Agent --> Out[Out]
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style In fill:#8B0000,color:#fff
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style Agent fill:#2E8B57,color:#fff
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style Tool fill:#3776AB,color:#fff
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style Out fill:#8B0000,color:#fff
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```
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## Quick Start
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<Steps>
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<Step title="Create MCP Server">
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Create a new file `app.py` with your custom MCP server implementation:
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```python
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import yfinance as yf
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from mcp.server.fastmcp import FastMCP
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mcp = FastMCP("stock_prices")
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@mcp.tool()
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async def get_stock_price(ticker: str) -> str:
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"""Get the current stock price for a given ticker symbol.
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Args:
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ticker: Stock ticker symbol (e.g., AAPL, MSFT, GOOG)
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Returns:
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Current stock price as a string
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"""
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if not ticker:
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return "No ticker provided"
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try:
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stock = yf.Ticker(ticker)
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info = stock.info
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current_price = info.get('currentPrice') or info.get('regularMarketPrice')
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if not current_price:
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return f"Could not retrieve price for {ticker}"
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return f"${current_price:.2f}"
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except Exception as e:
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return f"Error: {str(e)}"
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if __name__ == "__main__":
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mcp.run(transport='stdio')
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```
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</Step>
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<Step title="Install Dependencies">
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Install the required dependencies in a conda environment:
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```bash
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conda create -n mcp python=3.10
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conda activate mcp
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pip install yfinance mcp-python-sdk
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```
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</Step>
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<Step title="Create Agent Integration">
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Create a new file `stock_agent.py` with the following code:
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```python
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from praisonaiagents import Agent, MCP
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agent = Agent(
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instructions="""You are a helpful assistant that can check stock prices and perform other tasks.
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Use the available tools when relevant to answer user questions.""",
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llm="gpt-4o-mini",
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tools = MCP("/path/to/python /path/to/app.py")
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)
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# NOTE: Replace with your actual Python path and app.py file path
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agent.start("What is the stock price of Tesla?")
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```
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</Step>
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<Step title="Run the Agent">
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Execute your script:
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```bash
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zsh -c "source $(conda info --base)/etc/profile.d/conda.sh && conda activate windsurf && python stock_agent.py"
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```
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</Step>
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</Steps>
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<Note>
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**Requirements**
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- Python 3.10 or higher
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- Conda for environment management
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- yfinance package for stock data
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- mcp-python-sdk for MCP server implementation
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- OpenAI API key (for the agent's LLM)
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</Note>
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## Features
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<CardGroup cols={2}>
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<Card title="Custom Tools" icon="wrench">
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Create your own custom tools with Python.
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</Card>
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<Card title="MCP Integration" icon="plug">
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Seamless integration with Model Context Protocol.
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</Card>
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<Card title="Type Hints" icon="code">
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Strong typing for better code reliability.
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</Card>
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<Card title="Async Support" icon="bolt">
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Built-in support for asynchronous functions.
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</Card>
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</CardGroup>
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## Implementation Details
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### FastMCP Class
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The `FastMCP` class from the `mcp-python-sdk` package provides a simple way to create MCP servers in Python:
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```python
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from mcp.server.fastmcp import FastMCP
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# Create an MCP server with a name
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mcp = FastMCP("my_tools")
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# Define a tool using the @mcp.tool decorator
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@mcp.tool()
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async def my_tool(param1: str, param2: int) -> str:
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"""Tool description with clear documentation.
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Args:
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param1: Description of param1
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param2: Description of param2
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Returns:
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Description of the return value
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"""
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# Tool implementation
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return f"Processed {param1} with {param2}"
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# Run the server with stdio transport
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if __name__ == "__main__":
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mcp.run(transport='stdio')
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```
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### Agent Integration
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To use your custom MCP server with PraisonAI agents, use the `MCP` class to specify the command to run your Python script:
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```python
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from praisonaiagents import Agent, MCP
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agent = Agent(
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instructions="Agent instructions",
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llm="gpt-4o-mini",
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tools=MCP(
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command="python", # Or full path to Python
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args=["path/to/your/mcp_server.py"] # Path to your MCP server script
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)
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)
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```

‎docs/mint.json‎

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"name": "Tools",
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"url": "/tools"
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},
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{
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"name": "MCP",
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"url": "/mcp"
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},
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{
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"name": "JS",
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"url": "/js"
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"agents/recommendation"
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]
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},
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{
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"group": "MCP",
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"pages": [
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"mcp/bravesearch",
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"mcp/airbnb",
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"mcp/custom"
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]
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},
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{
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"group": "Tools",
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"pages": [

‎src/praisonai-agents/mcp-mini-airbnb.py‎

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from praisonaiagents import Agent, MCP
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search_agent = Agent(
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instructions="""You help book apartments on Airbnb.""",
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llm="gpt-4o-mini",
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tools=MCP("npx -y @openbnb/mcp-server-airbnb --ignore-robots-txt")
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)
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search_agent.start("I want to book an apartment in Paris for 2 nights. 03/28 - 03/30 for 2 adults")

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