How we think about AI context management, MCP servers, prompt versioning, semantic search, and the integration layer between Claude, Cursor, ChatGPT, and every other AI client your team already uses.
How Context Repo's reason tool turns retrieved document evidence into synthesized answers with citations, gaps, and conflicts, and why that costs more than returning ranked chunks.
How Context Repo makes itself readable to AI agents through llms.txt, well-known metadata, MCP discovery, OpenAPI, markdown negotiation, and feature-specific agent routes, with Ora.ai as a third-party readiness benchmark.
How Context Repo's Deep Search keeps document structure in the retrieval loop, why that improves recall for AI agents, and why it costs more than keyword search, one-vector-per-document search, or flat chunk RAG.
A grounded look at the Model Context Protocol, what an MCP server actually does, and how Context Repo's 28-tool MCP server connects Claude, Cursor, ChatGPT, and 90+ AI clients to your context repository.
How a context repository handles the day-to-day mechanics of prompts and documents that AI agents actually consume: version history, variable interpolation, semantic search, and 75+ file formats over MCP and REST.
Why Context Repo ships both a catalog-level semantic search and a hierarchical chunk navigator, and how to pick the right one for the question your AI agent is actually asking.
How a single context repository travels across Cursor, Claude Desktop, Claude.ai, and ChatGPT. Real workflows for the engineer, the researcher, and the operator, plus the Chrome extension that feeds all three.
What an AI context repository is, what belongs inside it, and how Context Repo gives humans and agents one home for prompts, documents, and collections across every AI tool.