Explore the Practical Guide to Building an MCP Dashboard

Before you begin, ensure you have the following prerequisites and technical requirements in place to successfully implement and work with the MCP dashboard system:

1. Install Visual Studio Code: https://code.visualstudio.com/

2. Install Python: https://www.python.org/downloads/

3. Install Claude Desktop App: https://claude.ai/download

Detailed Step-by-Step Implementation Guide

1. Create a new directory named "mcptool" in your C:\ drive by running the command: mkdir mcptool

2. Open Visual Studio Code, click File, and select Open Folder.

3. Select the folder “mcptool“.

4. Select "New Terminal" from the Menu.

5. Type the following commands in your terminal:

PS C:\mcptool>mkdir perplexity-search and press enter
PS C:\mcptool>cd perplexity-search and press enter
PS C:\mcptool\perplexity-search>python -m venv .venv and press enter
PS C:\mcptool\perplexity-search>.venv\Scripts\activate (For Windows) or source .venv\bin\activate (For Mac) and press enter

(.venv) PS C:\mcptool\perplexity-search> pip install requests and press enter
(.venv) PS C:\mcptool\perplexity-search> pip install mcp and press enter

6. Create a new file called "server.py" and paste the following Python code into Visual Studio Code.

import json
import requests
from mcp.server.fastmcp import FastMCP
import time
import signal
import sys

# === Configuration ===

PERPLEXITY_API_KEY = "PERPLEXITY_API_KEY"
PERPLEXITY_BASE_URL = "https://api.perplexity.ai"
PERPLEXITY_MODEL = "sonar-pro"

def submit_to_perplexity(query):
    headers = {
        "Content-Type": "application/json",
        "Authorization": f"Bearer {PERPLEXITY_API_KEY}"
    }

    messages = [
        {
            "role": "system",
            "content": "You are a search assistant. Provide markdown-formatted results with citations."
        },
        {
            "role": "user",
            "content": query
        }
    ]

    payload = {
        "model": PERPLEXITY_MODEL,
        "messages": messages,
        "max_tokens": 4096,  # Reduced for safety
        "temperature": 0.2,
        "top_p": 0.9,
        "stream": False
    }

    endpoint = f"{PERPLEXITY_BASE_URL}/chat/completions"
    response = requests.post(endpoint, headers=headers, json=payload)
    
    if response.status_code != 200:
        print(f"Debug - Failed Response: {response.text}")  # Add debugging
    
    response.raise_for_status()
    return response.json()



# Handle SIGINT (Ctrl+C) safely
def signal_handler(sig, frame):
    print("Safely stopping the server ...")
    sys.exit(0)

signal.signal(signal.SIGINT, signal_handler)

# Create an MCP server with increased timeout
mcp = FastMCP(
    name="perplexity-search",
    host="127.0.0.1",
    port=8080,
    timeout=30000  # Increase timeout to 30 seconds
)

# Define our tool.
# This tool now accepts a search query and uses the Perplexity API to retrieve search results.


@mcp.tool()
def perplexity_search(query: str):
    """
    Use the Perplexity API to perform a search for the given query.
    """
    try:
        if not isinstance(query, str):
            return {"error": "Input must be a string."}
        # Build the data structure expected by the Perplexity API.
        # final_data = {"query": query}  # No longer needed
        result = submit_to_perplexity(query)  # Pass the query directly
        return result
    except Exception as e:
        return {"error": str(e)}


if __name__ == "__main__":
    try:
        print("Starting MCP server 'perplexity-search' on 127.0.0.1:8080 ...")
        mcp.run()
    except Exception as e:
        print(f"Error: {e}")
        time.sleep(5)

7. Get your Perplexity API key from https://www.perplexity.ai/settings/api and replace the placeholder in the Python code: PERPLEXITY_API_KEY = "PERPLEXITY_API_KEY"

8. Open the Claude Desktop App, click the hamburger menu, select File, then go to Settings.

9. Select the Developer menu and click "Edit Config".

10. Select the file name: claude_desktop_config.json and double click on the file to open it.

11. Copy the configuration file and past it into the file: claude_desktop_config.json and save it.

{
  "mcpServers": {
    "perplexity-search": {
      "command": "python",
      "args": [
        "C:\\mcptool\\perplexity-search\\server.py"
      ],
      "host": "127.0.0.1",
      "port": 8080,
      "timeout": 30000
    }
  }
}

12. Execute the MCP server by running the following command in your terminal: python server.py If successful, you'll see this message: Starting MCP server 'perplexity-search' on 127.0.0.1:8080 ...

13. Open the Claude Desktop App, click the hamburger menu, select "File," then go to "Settings." Select "Developer" and choose perplexity-search. If successful, you will see a "running" status.

14. Exit the Developer menu. You will then see a hammer icon with "1 MCP tool available" in the chat box.

15. Click on the hammer icon to see the perplexity_search tool available.

16. Use this prompt to generate the MCP adoption dashboard.

Using the perplexity-search tool to analyze MCP server adoption across business sectors:
1. Gather statistical data on MCP server implementations by industry
2. Analyze the correlation between business types and MCP server usage patterns
3. Create data visualizations to illustrate key trends and relationships
4. Generate insights on sector-specific adoption rates and use cases

17. The visualization will display MCP server adoption patterns across different business sectors.

Key Takeaways

• MCP (Model Context Protocol) enables seamless integration between AI systems and real-time external data, addressing traditional integration challenges

• The protocol offers a standardized approach that eliminates the need for costly bespoke integrations across different systems

• Implementation requires minimal setup with standard development tools (VS Code, Python) and API configurations

• The practical guide demonstrates how to build an MCP dashboard using Perplexity API integration

• MCP represents a strategic advancement for organizations seeking to scale their AI implementations efficiently