mcp-memory-gateway

Capture an up/down signal plus one line of why. Vague feedback is logged, then returned with a clarification prompt instead of memory promotion.

Get summary of recent feedback

Get feedback stats and recommendations

Diagnose a failed or suspect workflow step using MCP schema, workflow, gate, and approval constraints.

List available intent plans and whether each requires human approval in the active profile

Generate an intent execution plan with policy checkpoints

Start a sequential delegation handoff from a delegation-eligible intent plan

Complete a sequential delegation handoff and record verification outcomes

Get the definition and state of a business entity (Customer, Revenue, Funnel). Aliased to describe_semantic_entity.

Retrieve active prevention rules and success patterns. Aliased to prevention_rules.

Capture success/failure feedback to harden future workflows. Aliased to capture_feedback.

Normalize a GitHub/Slack/Linear trigger into startup context, construct a recall pack, prepare a git worktree sandbox, and emit an execution plus reviewer-lane plan.

Generate prevention rules from repeated mistake patterns

Export DPO preference pairs from local memory log

Export RLHF logs and proof artifacts as a Databricks-ready analytics bundle

Construct a bounded context pack from contextfs

Record evaluation outcome for a context pack

Get recent context/provenance events

Auto-generate Claude skills from repeated feedback patterns. Clusters failure patterns by tags and produces SKILL.md files with DO/INSTEAD rules.

Recall relevant past feedback, memories, and prevention rules for the current task. Call this at the start of any task to inject past learnings into the conversation.

Satisfy a gate condition (e.g., after checking PR threads). Evidence is stored with a 5-minute TTL.

Get gate enforcement statistics -- blocked count, warned count, top gates

Get full RLHF dashboard -- approval rate, gate stats, prevention impact, system health

Recall past feedback filtered by commerce categories (product_recommendation, brand_compliance, sizing, pricing, regulatory). Returns quality scores alongside memories for agentic commerce agents.

Retrieve high-level business metrics (Revenue, Conversion, Customers) from the Semantic Layer.

Get the canonical definition and state of a business entity (Customer, Revenue, Funnel).

Estimate Bayesian uncertainty for a set of tags based on past feedback.

Write a session handoff primer that auto-captures git state (branch, last 5 commits, modified files), last completed task, next step, and blockers. The next session reads this automatically for seamless context continuity.

Read the most recent session handoff primer to restore context from the previous session. Call at session start.