Definitions

AI context definitions

Short canonical pages that define the vocabulary around temporal context graphs, content streams, agent context, MCP, and public JSON panels.

Temporal context graph A temporal context graph is a time-aware map of sources, entities, claims, signals, briefs, streams, and stable identifiers that lets AI systems reason with fresh context instead of isolated chunks.
Context for AI agents Context for AI agents is the fresh, structured evidence an agent can use to decide what matters now: sources, entities, timestamps, recurring topics, claims, and stable IDs.
Content streams for AI Content streams for AI are continuously refreshed, structured groupings of source signals, entities, and topics that help models understand a domain as it changes.
Structured news for LLMs Structured news for LLMs turns changing public information into citable signals with source, entity, timestamp, topic, and claim structure.
Stable IDs for AI data feeds Stable IDs let agents cite, compare, and revisit context as sources, streams, tags, and panels change over time.
MCP vs REST for agent context MCP and REST are delivery patterns for agent context: MCP fits tool-using agents, while REST fits application services and backend workflows.
Public JSON panels for LLM citation Public JSON panels are static, citable mirrors of human-readable context pages, designed so LLMs can inspect the same aggregate snapshot a person sees.