CouchLoop EQ - MCP Server

Behavioral governance layer for safer, more consistent AI conversations.

<p align="center"> <img src="https://raw.githubusercontent.com/wisenbergg/couchloop-mcp/master/assets/logo/couchloop_EQ-IconLogo.png" alt="CouchLoop EQ" width="120" /> </p>

<p align="center"> <a href="https://www.npmjs.com/package/couchloop-eq-mcp"><img src="https://img.shields.io/npm/v/couchloop-eq-mcp.svg" alt="npm version"></a> <a href="LICENSE"><img src="https://img.shields.io/badge/License-MIT-yellow.svg" alt="License: MIT"></a> <a href="https://github.com/wisenbergg/couchloop-mcp"><img src="https://img.shields.io/github/stars/wisenbergg/couchloop-mcp?style=social" alt="GitHub stars"></a> </p>

<p align="center"> <a href="https://mcp.couchloop.com">🌐 Landing Page</a> • <a href="https://www.npmjs.com/package/couchloop-eq-mcp">📦 npm</a> • <a href="https://github.com/wisenbergg/couchloop-mcp">⭐ GitHub</a> </p>

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📖 Choose Your Guide

Use Case	Guide	Description
💻 Developers	README-DEVELOPER.md	Package validation, security scanning, code review, context preservation
🧘 Wellness	README-WELLNESS.md	Guided sessions, journeys, insights, reflection tools

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What is CouchLoop EQ?

CouchLoop EQ is a behavioral governance layer for AI assistants. Built on MCP (Model Context Protocol), it monitors for hallucination, inconsistency, and unsafe reasoning patterns while managing stateful AI sessions.

Why CouchLoop EQ?

Unlike raw LLMs that can hallucinate packages, generate insecure code, and lose context mid-conversation, CouchLoop EQ catches problems before they ship:

Problem	CouchLoop EQ Solution
🎭 Hallucinated packages	review(mode: "packages") catches fake npm/PyPI/Maven before install
🔓 Insecure code	review(mode: "code") detects SQLi, XSS, hardcoded secrets
📉 Code bloat	review(mode: "code") flags over-engineering, console.logs, missing errors
🧠 Lost context	memory stores architecture decisions and checkpoints across sessions
📚 Deprecated APIs	review(mode: "packages") warns about outdated versions and breaking changes
🔍 Sloppy AI code	review(mode: "verify") pre-checks AI responses for hallucinated APIs
🧠 Session continuity	conversation with crisis detection, journeys, and session memory

Architecture

CouchLoop EQ uses a modular pipeline:

Request -> Classify -> Policy -> Plan -> Execute -> Compose

• Intent Classifier: Confidence-based routing with multi-intent detection
• Policy Engine: Health-aware routing with fallbacks and crisis override
• Execution Planner: DAG generation for parallel operations
• Tool Registry: Health tracking and circuit breakers
• OpenTelemetry: Distributed tracing across all stages

Key Safety Features

Behavioral Governance

• Hallucination Detection: Monitors for fabricated facts and unsupported claims
• Consistency Checking: Identifies contradictions and logical incoherence across turns
• Tone Monitoring: Detects emotional escalation, manipulation, or dependency-forming language
• Safety Guardrails: Prevents harmful advice, clinical overreach, and inappropriate moralizing

Session Management

• Stateful Conversations: Maintains context across multiple interactions
• Progress Tracking: Remember where users left off in guided journeys
• Crisis Detection: Integrated crisis detection with guided self-reflection journeys
• Memory Context: Preserves important insights and checkpoints

Privacy by Design

• No personal data stored: No emails, names, passwords, or API keys
• Session-based isolation: Each session is anonymous and isolated
• Your data stays yours: Insights and context are tied to session IDs, not identities
• No tracking: No analytics, no telemetry, no third-party data sharing

Quick Start

CouchLoop EQ is a standard MCP server that works with any MCP-compatible client - Claude Desktop, ChatGPT, Cursor, Windsurf, VS Code, and more.

Option 1: Connect to Hosted Server (Easiest)

Production endpoint: https://mcp.couchloop.com/mcp

For Claude Desktop (v0.7.0+), add to ~/Library/Application Support/Claude/claude_desktop_config.json:

{
  "mcpServers": {
    "couchloop-eq": {
      "url": "https://mcp.couchloop.com/mcp",
      "transport": "streamable-http"
    }
  }
}

Restart Claude and try: "Start a daily reflection session"

Option 2: Run Locally

npm install -g couchloop-eq-mcp

Add to Claude Desktop configuration:

{
  "mcpServers": {
    "couchloop-eq": {
      "command": "couchloop-eq-mcp",
      "env": {
        "COUCHLOOP_SERVER": "https://mcp.couchloop.com"
      }
    }
  }
}

Sessions automatically persist locally to ~/.couchloop-mcp/identity.json - no signup required.

For ChatGPT (Developer Mode)

ChatGPT supports MCP servers through Developer Mode. See CHATGPT_SETUP.md for detailed setup instructions.

For Other MCP Clients

Any MCP-compatible client (Cursor, Windsurf, Continue, etc.) can connect using:

• URL: https://mcp.couchloop.com/mcp
• Transport: streamable-http
• Auth: None required (session-based isolation)

Production Server Available: https://mcp.couchloop.com/mcp

Quick steps:

1. Enable Developer Mode in ChatGPT Settings
2. Add as MCP connector with URL: https://mcp.couchloop.com/mcp
3. No authentication required - uses session-based isolation

For local development:

• Use ngrok or deploy your own server
• Follow setup in CHATGPT_SETUP.md

Available Tools (10 Primary)

CouchLoop EQ provides 10 MCP tools with intelligent routing. The couchloop intent router handles ambiguous or multi-intent requests, while high-confidence intents can call tools directly.

Universal Entry Point

Tool	Description
couchloop	Intent router - Routes any loose command to the right tool. Use for: "end session", "save this", "review code", "brainstorm this", "help me", etc.

Governance

Tool	Description
guard	Per-turn governance - Evaluates draft responses before delivery. Actions: pass, modified, or blocked

Core Tools

Tool	Description
verify	Pre-delivery verification - Catches AI hallucinations, validates packages, checks code before presenting to users
status	Dashboard - Session progress, history, context window usage, protection status, preferences
conversation	AI conversation with crisis detection and session memory. Actions: start, send, end, resume, status
brainstorm	Standalone dev thinking partner - trade-off analysis, feature design, architecture decisions
code_review	Complete code analysis - security vulnerabilities, code smells, AI-generated errors
package_audit	Dependency audit - validates packages exist, checks versions, finds vulnerabilities
remember	Save and recall context, checkpoints, insights across sessions
protect	File protection - backup, freeze, rollback, restore

Usage Examples

# Via intent router (recommended for loose commands)
"end session"          -> couchloop routes to conversation(action: end)
"save this for later"  -> couchloop routes to remember(action: save)
"review my code"       -> couchloop routes to code_review
"brainstorm a feature" -> couchloop routes to brainstorm
"what can you do"      -> couchloop returns capabilities list

# Direct tool calls (for precise control)
conversation(action: "start", message: "Begin daily reflection")
brainstorm(message: "Design a caching layer")  # Dev ideation
remember(action: "recall")  # Get saved context
verify(type: "packages", content: "lodash-utils")  # Validate before install
code_review(code: "function foo()...")  # Analyze code

Real-World Usage

CouchLoop EQ is actively used in production development. Here is what 2 weeks of actual usage looked like:

Usage Statistics

Metric	Value
Insights captured	49
Active sessions	5
Unique tags	85+
Date range	Jan 19 - Feb 2, 2026

Development Areas Tracked

Category	Insights	Example
🔐 Security fixes	12	Auth flow hardening, validation improvements
💳 Payment integration	8	Payment flow patterns, webhook handling
📱 Mobile development	15	State management, navigation guards
🗄️ Database operations	6	Data cleanup, schema optimization
🏗️ Architecture decisions	8	Caching strategies, event patterns

Featured Insight: Complex Bug Resolution

PAYMENT FLOW BUG ROOT CAUSE IDENTIFIED:

Issue: Race condition between frontend state and backend data caused
inconsistent user experience during payment retry flows.

Analysis: Traced through 5 components across iOS and backend to find
the state synchronization gap.

FIX OPTIONS:
A) Data cleanup - reset stale records
B) Frontend fix - stricter validation
C) Backend fix - additional verification step

Recommended: Defense-in-depth approach combining A + B

This insight was captured mid-debugging session, preserved across context window resets, and referenced 3 days later when implementing the fix.

Best Practices for Sprint Development

Start of sprint: Create a session to establish context

"Create a session for Sprint 42 - user authentication overhaul"

After completing a feature: Save insights, context, or checkpoints depending on complexity

Feature Size	Recommended Actions
Small fix	memory(action: "save") with type insight — Quick note of what was done and why
Medium feature	memory(action: "save") with type insight + checkpoint — Capture decisions and state
Large feature set	Multiple memory saves (insight, checkpoint, decision) — Full architecture context

Why this matters: When you need to review or debug later, you can retrieve the exact context of what was just built - even weeks later, across different AI sessions.

memory(action: "recall") -> Instant recall of decisions made
"Resume my Sprint 42 session" -> Pick up exactly where you left off

Available Journeys

• Daily Reflection (5 min) - A brief check-in to process your day
• Gratitude Practice (3 min) - Notice and name three things you appreciate
• Weekly Review (10 min) - Look back on your week and set intentions

Example Usage

Start a daily reflection:

"Start a daily reflection session"

Resume where you left off:

"Resume my last session"

Save an insight:

"Save this insight: I notice I'm more energized in the mornings"

Screenshots

<p align="center"> <img src="assets/screenshots/save_insights.png" alt="Save Insights" width="400" /> <img src="assets/screenshots/checkpoint_session.png" alt="Checkpoint Session" width="400" /> <img src="assets/screenshots/code_review_workflow.png" alt="Code Review Workflow" width="400" /> </p>

Support

• Issues: github.com/wisenbergg/couchloop-mcp/issues
• Email: support@couchloop.com

License

MIT