From Zero to MCP Server in 30 Minutes

MCP (Model Context Protocol) is becoming the standard way for AI applications to call external tools. Here's how to build and deploy an MCP server for SERP search.

What Is MCP?
MCP is a protocol that lets AI models (Claude, ChatGPT, etc.) discover and use tools through a standardized interface. Think of it as "USB-C for AI tools."

The Server Code

# talordata_mcp/server.py
import asyncio
from mcp.server import Server, Tool
from talordata import TalorClient

client = TalorClient(api_key=os.environ["TALOR_API_KEY"])
server = Server("talordata-search")

@server.tool()
async def search(query: str, engine: str = "google") -> list:
    """Search the web and return structured results."""
    results = await client.async_search(q=query, engine=engine)
    return [
        {"title": r.title, "link": r.link, "snippet": r.snippet}
        for r in results.organic[:10]
    ]

if __name__ == "__main__":
    server.run()

Deployment Options
Option Best For
Local Development and testing
Docker Production deployment
MCP Registry Public discovery

Why Build an MCP Server?

• AI applications can use your tool without custom integration
• One server works with Claude, Cursor, LangChain, and more
• Your tool becomes discoverable through MCP directories

Distribution Checklist
Platform Article # Notes
Dev.to 1, 2, 3, 5 Add #langchain #rag #ai tags
Medium 1, 2, 3, 4 Publish to relevant publications
Hashnode All Cross-post from Dev.to
Hacker News 3, 5 "Show HN" or discussion threads
Indie Hackers 3, 4 Share pricing/cost analysis
Quora All Answer relevant questions with article links

Need me to write additional articles on specific topics (e.g., n8n integration, TypeScript examples, or LlamaIndex)? Just let me know. 😊