AI_CONTEXT schema context formatter and MCP resources built

ID	M0-AICONTEXT-002
Status	completed
Priority	p0
Milestone	m0-prototype
Owner	data-ai-engineer-architect
Initiative	mcp-catalog-agent-tools

Problem

Raw catalog metadata was too verbose and unstructured for AI agent consumption — dumping full profiling output into agent context burned excessive tokens and buried the most relevant information. There was no formatter that could produce token-efficient, semantically organized schema context, and no MCP resource endpoints to expose pre-built context payloads. Agents had to either receive bloated context or manually filter raw metadata, degrading both cost efficiency and response quality.

Context

Token-efficient schema context formatting is implemented and used by MCP.
Core MCP resources expose schema context, YAML reference, and guides.
Documentation reflects available resources and consumption model.

Possible Solutions

Plan

Keep resource docs/examples current as payload contracts evolve.
Ensure resource availability is validated in smoke tests.
Track only net-new improvements in later milestone tasks.

Implementation Progress

Prototype baseline recorded as completed and linked to follow-on milestone work.
Remaining scope is tracked in explicit M1+ tasks rather than implicit debt.
AI Context Architecture current-status table

Review Feedback

[ ] Review cleared