MCP GoGrab AI — Decision & Logging Execution Protocol

Purpose

This document defines the mandatory process for making, validating, logging, and reinforcing development decisions inside mcp-gograb-ai.

The objective is not just to solve a problem.

The objective is to:

• Make high-confidence decisions (≥ 0.9 calibrated)
• Record institutional knowledge
• Reinforce or correct outcomes
• Strengthen long-term system intelligence

This protocol must be followed for:

• Architecture decisions
• Bug fixes
• Refactors
• Pattern adoptions
• Convention changes
• Performance optimizations
• Module-level implementations
• Strategic technical pivots

1. Core Principles

1. No non-trivial change without context.
2. No decision without reasoning.
3. No reasoning without calibration.
4. No decision without logging.
5. No logging without outcome tracking.
6. No outcome without reinforcement or correction.

If any of these are skipped, the intelligence loop is broken.

2. Phase 1 — Context Enforcement (Mandatory)

Before making any meaningful decision:

preflight({ task: "<clear task description>" })

If similar issues may exist:

find_similar({ query: "<problem summary>" })

If conventions may apply:

convention({ action: "get" })

If the decision touches architecture or prior patterns:

intelligence({ action: "recall", situation: "<context>" })

Never decide in isolation.

3. Phase 2 — Decision Framing

Before selecting a solution, explicitly define:

• What exactly is the problem?
• What is the root cause?
• What constraints exist?
• What are viable alternatives?
• What are long-term risks of each alternative?

If multiple valid solutions exist:

intelligence({
  action: "simulate",
  simulateAction: "<chosen approach>",
  context: "<system constraints>",
  alternatives: ["alt1", "alt2"]
})

This step is required for:

• Architectural shifts
• State management changes
• Platform mutations
• Multi-module refactors

4. Phase 3 — Confidence Calibration (Required)

Before implementation:

intelligence({
  action: "calibrate",
  rawConfidence: 0.X,
  domain: "<architecture | angular | platform | performance>"
})

Interpretation:

• ≥ 0.9 → Safe to proceed
• 0.75–0.89 → Acceptable but requires monitoring
• < 0.75 → Insufficient certainty, continue research

No architectural decision should proceed below 0.85.

5. Phase 4 — Implementation & Validation

Perform the work.

If code-related:

code({ action: "review", target: "...", reviewType: "full" })
code({ action: "validate", modulePath: "...", strictMode: true })

If platform-related:

platform({ action: "mutate", operation: "...", entity: "...", data: {...} })

If multi-agent coordination required:

agent({ action: "handoff_create", ... })

Validation must precede logging.

6. Phase 5 — Decision Logging (Mandatory)

Immediately after validation:

log_decision({
  type: "architecture | implementation | bugfix | refactor | pattern | convention | documentation | milestone",
  question: "<what decision was being made?>",
  chosen: "<what was selected?>",
  reasoning: "<root cause explanation and tradeoff analysis>",
  confidence: 0.9,
  options: ["alternative1", "alternative2"],
  files: ["fileA.ts", "fileB.ts"],
  task: "<task context>"
})

Minimum logging requirements:

• Clear problem statement
• Alternatives considered
• Explicit tradeoffs
• Root cause reasoning
• Calibrated confidence
• Affected files or modules

Do not log vague summaries.

7. Phase 6 — Reinforcement Loop (Outcome Tracking)

After deployment, test validation, or real-world usage:

outcome({
  action: "report",
  decisionId: "<id>",
  wasSuccessful: true | false,
  feedback: "<what actually happened>",
  rating: 1-5
})

If module-level:

outcome({
  action: "module",
  moduleName: "<module>",
  outcome: "success | partial | failed"
})

If triggered by system events:

outcome({
  action: "track",
  decisionId: "<id>",
  triggerType: "git_commit | build | test"
})

No decision is complete without outcome reporting.

8. Phase 7 — System Hardening (If Needed)

If the issue exposed structural weakness:

Add Convention

convention({
  action: "add",
  category: "<angular | testing | architecture>",
  rule: "<clear enforceable rule>",
  examples: ["example snippet"],
  source: "decision <id>"
})

Reflect for Learning

intelligence({
  action: "reflect",
  taskSummary: "...",
  outcome: "...",
  lessonsLearned: ["lesson1", "lesson2"]
})

Detect Gaps

intelligence({ action: "gaps", domain: "..." })

This transforms fixes into systemic improvement.

9. Decision Classification Rules

Misclassification weakens future recall accuracy.

10. Anti-Patterns (Prohibited)

• Logging without calibrated confidence.
• Logging without alternatives.
• Skipping outcome reporting.
• Overriding sessions improperly.
• Making architectural changes without simulation.
• Logging trivial changes.

If the change does not meaningfully affect behavior, do not log it.

11. Minimal Required Flow (Quick Reference)

preflight({ task: "..." })
find_similar({ query: "..." })
intelligence({ action: "simulate", ... })
intelligence({ action: "calibrate", rawConfidence: 0.X, domain: "..." })
... implement ...
log_decision({ ... })
outcome({ action: "report", ... })

If confidence < 0.9 → Do not finalize.

If outcome fails → Reflect + adjust.

12. System-Level Goal

Following this protocol ensures:

• Decisions become institutional memory
• Confidence becomes measurable
• Agents improve over time
• Anti-patterns are detected early
• Architecture converges toward stability
• Reinforcement learning loops stay intact

This is not just logging.

This is structured intelligence evolution.

End of Protocol.