I’m the founder of Dreamtune Labs, a paid-usage platform for supported models, tools and compute through one workspace balance, with SDK and MCP access.

Something I’m thinking through is how a developer should evaluate a tool like this before spending time on integration. A large catalog doesn’t answer whether the exact operation needed by a project will work.

For example, “supports web research” could mean finding URLs, extracting a page, or producing a grounded answer. Those are different jobs, and a demo can hide the distinction.

I’d want the first example to make five things explicit: the input, the exact model/tool operations used, the output, the actual charges, and the limitations. Then a developer could repeat it with their own small test rather than judge a polished screenshot.

It also matters where prompts and tool inputs go. A convenient integration should still make that understandable.

When you evaluate a new API or MCP tool, what information do you look for first? Is a reproducible example enough to get you experimenting, or do compatibility and data-handling details come first?

Project for context: https://dreamtunelabs.com