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AI_CONTEXT schema context formatter and MCP resources built

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