Building an MCP server — lessons from thunderbit-mcp

📰 Dev.to AI

Learn how to build an MCP server by exposing a web extraction API to AI coding agents, and discover the key challenges and lessons from the thunderbit-mcp project

intermediate Published 11 May 2026

Action Steps

Design the architecture of your MCP server to expose the web extraction API to AI coding agents
Determine the tools and features to expose through the MCP server and define their return values for different scenarios
Implement error handling and edge cases, such as page blocking, slow extraction, or partial extraction
Configure and test the MCP server with AI coding agents to ensure seamless integration
Monitor and optimize the performance of the MCP server for production-ready deployment

Who Needs to Know This

This lesson is relevant for software engineers, DevOps teams, and AI engineers who want to build MCP servers and integrate AI coding agents with web extraction APIs. It provides valuable insights into the challenges and solutions for building a robust MCP server.

Key Insight

💡 The hardest parts of building an MCP server are not technical, but product-shaped, requiring careful consideration of tool exposure, error handling, and performance optimization

Key Takeaways

Learn how to build an MCP server by exposing a web extraction API to AI coding agents, and discover the key challenges and lessons from the thunderbit-mcp project

Full Article

When we started building thunderbit-mcp , the plan sounded straightforward: expose Thunderbit's web extraction API to AI coding agents through the Model Context Protocol. In practice, the hard parts were not the SDK calls. The hard parts were product-shaped: How many tools should the server expose? What should a tool return when a page is blocked, slow, or only partially extracted? Should t

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