Enhance MCP Documentation and Integration

docs/mcp/aws-kb-retrieval.mdx

@@ -0,0 +1,84 @@
+---
+title: "AWS KB Retrieval MCP Integration"
+sidebarTitle: "AWS KB Retrieval"
+description: "Guide for integrating AWS Knowledge Base retrieval capabilities with PraisonAI agents using MCP"
+icon: "aws"
+---
+
+## Add AWS KB Retrieval Tool to AI Agent
+
+```mermaid
+flowchart LR
+    In[Query] --> Agent[AI Agent]
+    Agent --> Tool[AWS KB Retrieval MCP]
+    Tool --> Agent
+    Agent --> Out[Answer]
+
+    style In fill:#8B0000,color:#fff
+    style Agent fill:#2E8B57,color:#fff
+    style Tool fill:#FF9900,color:#fff
+    style Out fill:#8B0000,color:#fff
+```
+
+## Quick Start
+
+<Steps>
+    <Step title="Install Dependencies">
+        Make sure you have Node.js installed, as the MCP server requires it:
+        ```bash
+        pip install praisonaiagents mcp
+        ```
+    </Step>
+    <Step title="Set API Keys">
+        Set your AWS credentials as environment variables in your terminal:
+        ```bash
+        export AWS_ACCESS_KEY_ID=your_aws_access_key_id_here
+        export AWS_SECRET_ACCESS_KEY=your_aws_secret_access_key_here
+        export AWS_REGION=your_aws_region_here
+        export OPENAI_API_KEY=your_openai_api_key_here
+        ```
+    </Step>
+
+    <Step title="Create a file">
+        Create a new file `aws_kb_retrieval.py` with the following code:
+        ```python
+        from praisonaiagents import Agent, MCP
+        import os
+
+        # Get AWS credentials from environment
+        aws_access_key = os.getenv("AWS_ACCESS_KEY_ID")
+        aws_secret_key = os.getenv("AWS_SECRET_ACCESS_KEY")
+        aws_region = os.getenv("AWS_REGION")
+
+        # Use a single string command with AWS KB Retrieval configuration
+        aws_kb_agent = Agent(
+            instructions="""You are a helpful assistant that can interact with AWS Knowledge Base.
+            Use the available tools when relevant to retrieve and process AWS information.""",
+            llm="gpt-4o-mini",
+            tools=MCP("npx -y @modelcontextprotocol/server-aws-kb-retrieval",
+                    env={
+                        "AWS_ACCESS_KEY_ID": aws_access_key,
+                        "AWS_SECRET_ACCESS_KEY": aws_secret_key,
+                        "AWS_REGION": aws_region
+                    })
+        )
+
+        aws_kb_agent.start("Search AWS documentation about EC2 instances")
+        ```
+    </Step>
+
+    <Step title="Run the Agent">
+        Execute your script:
+        ```bash
+        python aws_kb_retrieval.py
+        ```
+    </Step>
+</Steps>
+
+<Note>
+  **Requirements**
+  - Python 3.10 or higher
+  - Node.js installed on your system
+  - AWS credentials (Access Key ID, Secret Access Key, and Region)
+  - OpenAI API key (for the agent's LLM)
+</Note>

docs/mcp/bravesearch.mdx

@@ -5,14 +5,14 @@ description: "Guide for integrating Brave Search capabilities with PraisonAI age
 icon: "searchengin"
 ---
 
-# Brave Search MCP Integration
+## Add Brave Search Tool to AI Agent
 
 ```mermaid
 flowchart LR
-    In[In] --> Agent[AI Agent]
+    In[Query] --> Agent[AI Agent]
     Agent --> Tool[Brave Search MCP]
     Tool --> Agent
-    Agent --> Out[Out]
+    Agent --> Out[Answer]
 
     style In fill:#8B0000,color:#fff
     style Agent fill:#2E8B57,color:#fff
@@ -23,6 +23,12 @@ flowchart LR
 ## Quick Start
 
 <Steps>
+    <Step title="Install Dependencies">
+        Make sure you have Node.js installed, as the MCP server requires it:
+        ```bash
+        pip install praisonaiagents mcp
+        ```
+    </Step>
     <Step title="Set API Key">
         Set your Brave Search API key as an environment variable in your terminal:
         ```bash
@@ -54,13 +60,6 @@ flowchart LR
         ```
     </Step>
 
-    <Step title="Install Dependencies">
-        Make sure you have Node.js installed, as the MCP server requires it:
-        ```bash
-        pip install praisonaiagents
-        ```
-    </Step>
-
     <Step title="Run the Agent">
         Execute your script:
         ```bash
@@ -76,20 +75,3 @@ flowchart LR
   - Brave Search API key
   - OpenAI API key (for the agent's LLM)
 </Note>
-
-## Features
-
-<CardGroup cols={2}>
-  <Card title="Web Search" icon="globe">
-    Search the web for up-to-date information.
-  </Card>
-  <Card title="MCP Integration" icon="plug">
-    Seamless integration with Model Context Protocol.
-  </Card>
-  <Card title="Environment Variables" icon="key">
-    Secure API key handling through environment variables.
-  </Card>
-  <Card title="NPM Package" icon="js">
-    Leverages the official Brave Search MCP server package.
-  </Card>
-</CardGroup>

docs/mcp/custom-python-client.mdx

@@ -0,0 +1,103 @@
+---
+title: "Custom Python MCP Client"
+sidebarTitle: "Custom Python Client"
+description: "Guide for creating a client to interact with a custom Python MCP server"
+icon: "person-digging"
+---
+
+## Custom Python MCP Client
+
+```mermaid
+flowchart LR
+    In[Query] --> Agent[AI Agent]
+    Agent --> Client[Python MCP Client]
+    Client --> Server[Python MCP Server]
+    Server --> Client
+    Client --> Agent
+    Agent --> Out[Answer]
+
+    style In fill:#8B0000,color:#fff
+    style Agent fill:#2E8B57,color:#fff
+    style Client fill:#3776AB,color:#fff
+    style Server fill:#3776AB,color:#fff
+    style Out fill:#8B0000,color:#fff
+```
+
+## Overview
+
+The Custom Python MCP Client demonstrates how to integrate a custom Python MCP server with a PraisonAI agent. This client connects to a stock price MCP server to retrieve real-time stock information.
+
+## Quick Start
+
+<Steps>
+    <Step title="Install Dependencies">
+        Install the required packages:
+        ```bash
+        pip install praisonaiagents mcp
+        ```
+    </Step>
+    <Step title="Set Up the Server">
+        First, set up the Custom Python MCP Server.
+    </Step>
+    <Step title="Create Custom Python Client">
+        Save the code above to a file named `custom-python-client.py`.
+```python
+from praisonaiagents import Agent, MCP
+
+agent = Agent(
+    instructions="""You are a helpful assistant that can check stock prices and perform other tasks.
+    Use the available tools when relevant to answer user questions.""",
+    llm="gpt-4o-mini",
+    tools = MCP("/Users/praison/miniconda3/envs/mcp/bin/python /Users/praison/stockprice/custom-python-server.py")
+)
+
+# NOTE: Python Path replace with yours: /Users/praison/miniconda3/envs/mcp/bin/python
+# NOTE: custom-python-server.py file path, replace it with yours: /Users/praison/stockprice/custom-python-server.py
+
+agent.start("What is the stock price of Tesla?")
+```
+    </Step>
+    <Step title="Run the Client">
+        Execute the client script:
+        ```bash
+        python custom-python-client.py
+        ```
+    </Step>
+</Steps>
+
+<Note>
+  **Requirements**
+  - Python 3.10 or higher
+  - praisonaiagents and mcp packages
+  - A properly configured custom Python MCP server
+  - OpenAI API key (for the agent's LLM)
+</Note>
+
+## Environment Variables
+
+For better security and flexibility, you can modify the client to use environment variables:
+
+```python
+import os
+from praisonaiagents import Agent, MCP
+
+# Get paths from environment variables or use defaults
+python_path = os.getenv("PYTHON_PATH", "/path/to/python")
+server_path = os.getenv("SERVER_PATH", "/path/to/server.py")
+
+agent = Agent(
+    instructions="""You are a helpful assistant that can check stock prices and perform other tasks.
+    Use the available tools when relevant to answer user questions.""",
+    llm="gpt-4o-mini",
+    tools=MCP(f"{python_path} {server_path}")
+)
+
+agent.start("What is the stock price of Tesla?")
+```
+
+This approach allows you to set the paths using environment variables:
+
+```bash
+export PYTHON_PATH=/Users/praison/miniconda3/envs/mcp/bin/python
+export SERVER_PATH=/Users/praison/stockprice/app.py
+```

docs/mcp/custom-python-server.mdx

@@ -0,0 +1,86 @@
+---
+title: "Custom Python MCP Server"
+sidebarTitle: "Custom Python Server"
+description: "Guide for creating a custom Python MCP server for stock price retrieval"
+icon: "server"
+---
+
+## Custom Python MCP Server
+
+```mermaid
+flowchart LR
+    In[Query] --> Agent[AI Agent]
+    Agent --> Client[Python MCP Client]
+    Client --> Server[Python MCP Server]
+    Server --> Client
+    Client --> Agent
+    Agent --> Out[Answer]
+
+    style In fill:#8B0000,color:#fff
+    style Agent fill:#2E8B57,color:#fff
+    style Client fill:#3776AB,color:#fff
+    style Server fill:#3776AB,color:#fff
+    style Out fill:#8B0000,color:#fff
+```
+
+## Overview
+
+The Custom Python MCP Server is a simple implementation of the Model Context Protocol (MCP) that provides stock price information using the yfinance library. This server can be used with PraisonAI agents to retrieve real-time stock prices.
+
+## Server Implementation
+
+Below is the complete implementation of the custom Python MCP server:
+
+
+## Quick Start
+
+<Steps>
+    <Step title="Install Dependencies">
+        Install the required packages:
+        ```bash
+        pip install yfinance mcp
+        ```
+    </Step>
+    <Step title="Save the Server Code">
+        Save the code above to a file named `custom-python-server.py`.
+```python
+import yfinance as yf
+from mcp.server.fastmcp import FastMCP
+
+mcp = FastMCP("stock_prices")
+
+@mcp.tool()
+async def get_stock_price(ticker: str) -> str:
+    """Get the current stock price for a given ticker symbol.
+
+    Args:
+        ticker: Stock ticker symbol (e.g., AAPL, MSFT, GOOG)
+
+    Returns:
+        Current stock price as a string
+    """
+    if not ticker:
+        return "No ticker provided"
+    try:
+        stock = yf.Ticker(ticker)
+        info = stock.info
+        current_price = info.get('currentPrice') or info.get('regularMarketPrice')
+        if not current_price:
+            return f"Could not retrieve price for {ticker}"
+        return f"${current_price:.2f}"
+
+    except Exception as e:
+        return f"Error: {str(e)}"
+
+if __name__ == "__main__":
+    mcp.run(transport='stdio')
+```
+    </Step>
+</Steps>
+
+<Note>
+  **Requirements**
+  - Python 3.10 or higher
+  - yfinance package
+  - mcp package
+</Note>