How to Build a Claude Connector: MCP Server Tutorial (2026)
Adspirer Team
Learn how to build a working MCP server from scratch that connects Claude to your custom APIs, databases, or internal tools. This tutorial covers environment setup, tool creation, authentication, and connecting to Claude Desktop.
Want Claude to connect to your custom APIs, databases, or internal tools? You need to build an MCP server. MCP (Model Context Protocol) is the standard for extending Claude with new capabilities.
This tutorial walks you through building a working MCP server from scratch.
What you’ll build:
- A working MCP server that Claude can use
- Custom tools that call external APIs
- Proper authentication handling
Skip the build? Adspirer is a production MCP server for Google, Meta, Amazon, ChatGPT Ads, LinkedIn, and TikTok Ads — 400+ tools across six platforms. No coding required.
What is MCP?
MCP (Model Context Protocol) is an open protocol that standardizes how AI assistants connect to external tools. Think of it as a USB-C port for LLMs—a universal connector.
How It Works
Claude connects to your MCP server using the protocol. When a user asks Claude something, Claude calls your tools, your server executes the action, and returns results.
Quick Start: Python MCP Server
Set Up Your Environment
Set up your environment with uv and install the MCP SDK.
Create Your Server
Create a server.py file that initializes FastMCP and defines your tools.
Configure Claude Desktop
Configure Claude Desktop to connect to your server.
Connecting to Claude
Edit the Claude Desktop config file and add your MCP server details. Use absolute paths and restart Claude Desktop completely after making changes.
Always use absolute paths in the Claude Desktop config file. Relative paths will not resolve correctly and your server won’t connect.
Production Pattern: Tool Discovery at Scale
A server with three tools is easy — Claude sees all of them and picks the right one. But production servers grow. Once you cross a few dozen tools, exposing every schema at once bloats the context window and makes tool selection less reliable.
The pattern most large MCP servers converge on is a discovery layer. Instead of registering hundreds of tools directly, you expose two meta-tools: one that takes a natural-language description and returns the best-matching tool names (search_tools), and one that returns a specific tool’s exact parameters on demand (get_tool_schema). The model searches, reads the schema it needs, then calls the real tool — so context stays lean no matter how big the surface gets.
Adspirer uses exactly this pattern to keep 400+ tools across six ad platforms (Google, Meta, Amazon, ChatGPT Ads, LinkedIn, and TikTok) navigable inside a single connector. If your own server is heading past ~20 tools, design the discovery layer early — retrofitting it later is painful.
FAQ
Conclusion
Building an MCP server lets you extend Claude with custom capabilities. Start with the quick start examples, then build toward your specific use case.
Want MCP for advertising without building it? Adspirer is a production MCP server for Google Ads, Meta Ads, and TikTok Ads.
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