This topic covers the emerging practice of encoding “trust metadata” (provenance, attestations, data lineage and capability descriptors), automated risk‑scanning frameworks, and agent interoperability standards that let AI agents discover and safely reuse context across environments. The central problem is operational: as enterprises deploy many autonomous agents and multiple Model Context Protocol (MCP) servers, teams need machine‑readable signals about where context came from, which checks were run, and which endpoints an agent can trust. Relevance and timeliness (2026): the growth of production AI agents and regulatory pressure for auditability have made provenance, continuous risk scanning, and standardised agent handoffs first‑order concerns. Operators need observability into agent decisions and data flows, and data teams need lineage integrations so cataloged assets can be safely surfaced to agents. Key tools and roles: MCPJungle acts as a central registry and gateway for enterprise MCP servers, providing a single source of truth for available context providers. Wren Engine provides a semantic engine and MCP server components for rich context semantics. DataHub’s MCP Server integrates MCP with a metadata catalog to enable searchable assets and end‑to‑end lineage for agent queries. Context‑portal (ConPort) offers a lightweight, per‑workspace MCP backend for structured project memory and knowledge graphs. MCP Compass helps agents discover and select the most appropriate MCP server. Taken together, these components illustrate a practical stack: standardized MCP APIs + registries for discovery, metadata stores for provenance and lineage, and risk‑scanning layers that attach attestations to context. That combination supports stronger agent observability, safer context reuse, and clearer audit trails without relying on ad hoc integrations.