learning_ai_common_plat/docs/MCP+A2A/DOMAIN_EXTRACTION_SERVICE.md

Domain — extraction-service (MCP + A2A Opportunities)

Why extraction-service is ideal for MCP

It already provides:

• a single entrypoint (POST /extract, POST /extract/batch)
• async extraction jobs (/extract/jobs)
• model registry
• sidecar health monitoring and circuit breaker
• rate limits + quotas + cache

Agents can use MCP tools to iterate on prompts/tasks safely and repeatably.

High-value MCP tool proposals

Core extraction

• extraction.extract(text, taskId?, modelId?, productId?)
• extraction.extractBatch(inputs, modelId?)

Async jobs

• extraction.submitJob(inputs, modelId?, webhookUrl?)
• extraction.getJob(jobId)
• extraction.listJobs()

Observability

• extraction.sidecarHealth()
• extraction.metrics()
• extraction.cacheStats() (backs GET /extract/cache-stats)
• extraction.sidecarMonitoringState() (backs GET /extract/monitoring/sidecar)

Rate limits / admin utilities

• extraction.getProductRateLimitStatus(productId?)
• extraction.resetProductRateLimit(productId) (admin)

Recommended MCP resources

• extraction.modelRegistry
• extraction.taskCatalog
  • list task IDs used across products (triage, reflection-enrichment, memory-insight, etc.)
• extraction.promptGuidelines

Recommended A2A workflows

1) Task design loop

• TaskDesignerAgent drafts:
  • task prompt
  • a small set of examples
• EvalRunnerAgent runs:
  • extractBatch over an eval set
  • compares JSON shape correctness
• RegressionAgent checks:
  • no degradation vs previous baseline

2) Extraction incident response

• If extraction errors spike:
  • check sidecar health and circuit breaker state
  • reduce per-product rate limits
  • switch modelId (if supported)

Product integration hotspots

• MindLyst web API routes proxy to extraction-service (/api/extract and triage routes).
• Future: other products can standardize on the same tasks and use a shared task registry.