How We Give Claude Access to Real-Time Data

By combining Anthropic's Model Context Protocol with real-time structured web data powered by Supergood integrations, we dramatically improve the accuracy and usefulness of LLMs.

If you've ever tried to get an LLM to accurately utilize data from the web, you'll know the struggle.

Ask it to find menu prices from Chicago restaurants and you'll get a wall of text about Lou Malnati's famous deep dish instead of a clean table of restaurants, prices, and items.

We've been working with Claude to tackle this problem, and we've found that by combining Anthropic's Model Context Protocol (MCP) with real-time structured web data powered by Supergood integrations, we can dramatically improve the accuracy and usefulness of LLMs.

LLM + Web Data: Great for demos, not for scale

When using tools like Perplexity to find pets available for adoption in Burlington, NC, the results appear acceptable at first glance but prove sparse and inaccurate. In reality, dozens of animals available for adoption go undetected by such tools.

For teams building at scale, consumer-grade LLM subscriptions prove insufficient for integrating and analyzing web data into products.

Solving the scale problem requires better tools

Using Anthropic's MCP protocol with Supergood-generated tools produces striking improvements:

• Full control over data sources: Models prioritize high-quality structured datasets unavailable to off-the-shelf LLMs
• Search across massive datasets: Rather than cramming unstructured data into context windows

This extends beyond RAG applications and is a hard truth about LLMs — they work way better when they have access to structured data.

MCP bridges the gap between models and tools

MCP is Anthropic's protocol for standardizing how models interact with external tools. It functions as a universal adapter enabling models like Claude to connect seamlessly with specialized tools while maintaining consistent output formats.

Once tools implement MCP, any model supporting the protocol can use them, promoting better long-term interoperability across the AI ecosystem.

Structured, real-time web data: AI's killer power tool

Perplexity excels at providing humans quick web answers, but production AI applications require fundamentally different approaches. The challenge involves maintaining reliable data pipelines that deliver consistently high-quality output while handling modern web application complexity.

Supergood generates "unofficial APIs that are officially maintained." The platform combines LLMs with proprietary observability data and human-in-the-loop expertise to ensure high-quality, structured data delivery, allowing AI applications to utilize real-time web data without requiring dedicated engineering resources.

See it for yourself

MCP is still nascent, but its potential with reliable web data is evident. If your team is experimenting with AI agents and tools, we'd love to show you what's possible — reach out at [email protected].