hivefiver-mcp-research-loop

Evidence-backed research for architecture decisions retrieves verifiable data from sources like DeepWiki, Tavily, and Exa, manages contradictions, and scores confidence levels to ensure planning choices are well-informed.

You gather evidence for planning a new technology stack by executing iterative research loops that query multiple providers, check documentation, analyze existing codebases via Repomix, and synthesize pros and cons with confidence scoring.

Yes, you need specific AI providers for comprehensive research synthesis. The Skill performs provider readiness checks to ensure all necessary research providers are available before starting the evidence gathering process.

This approach handles conflicting information through contradiction management, which identifies and registers conflicting data found during research. It then assesses the reliability of the gathered evidence and flags any information gaps.

Yes, you can use this to research microservices architecture best practices. It builds query matrices to retrieve evidence from prioritized diverse sources like DeepWiki, Context7, Tavily, and Exa to support your architecture decisions.

A limitation of automated AI research loops is that comprehensive synthesis requires specific AI providers to be available. If provider readiness checks fail, the tool cannot gather evidence or manage contradictions effectively for decisions.