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couchloop-eq-mcp

Behavioral governance layer for LLMs - monitors for hallucination, inconsistency, tone drift, and unsafe reasoning while managing stateful AI sessions

npm28/wk
Updated
Mar 16, 2026

Quick Install

npx -y couchloop-eq-mcp

CouchLoop EQ — MCP Server

Behavioral governance layer for safer, more consistent AI conversations.

CouchLoop EQ

npm version License: MIT GitHub stars

🌐 Landing Page📦 npm⭐ GitHub


📖 Choose Your Guide

Use CaseGuideDescription
💻 DevelopersREADME-DEVELOPER.mdPackage validation, security scanning, code review, context preservation
🧘 WellnessREADME-WELLNESS.mdGuided sessions, journeys, insights, reflection tools

What is CouchLoop EQ?

CouchLoop EQ is an MCP (Model Context Protocol) server that provides behavioral governance for LLMs. It monitors AI responses for hallucination, inconsistency, tone drift, and unsafe reasoning patterns—while also managing stateful sessions and guided journeys that remember where you left off.

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:

ProblemCouchLoop EQ Solution
🎭 Hallucinated packagesverify + package_audit catch fake npm/PyPI/Maven before install
🔓 Insecure codecode_review detects SQLi, XSS, hardcoded secrets
📉 Code bloatcode_review flags over-engineering, console.logs, missing error handling
🧠 Lost contextremember stores architecture decisions and checkpoints across sessions
🗂️ Accidental deletionprotect with automatic backups, freeze mode, and rollback
📚 Deprecated APIspackage_audit warns about outdated versions and breaking changes
🔍 Sloppy AI codeverify pre-checks AI responses for hallucinated APIs and bad imports
💡 Unstructured thinkingbrainstorm helps think through trade-offs, compare options, decompose ideas

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 (v1.2.0)

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"
      }
    }
  }
}

New in v1.0.4: 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:

Available Tools (10 Primary)

CouchLoop EQ v1.4.0 uses a 10-tool architecture. The couchloop meta-tool acts as an intelligent router—just say what you want in natural language.

v1.4.0: All tools now execute through a mandatory policy layer (sanitize → verify-if-required → normalize → log), providing consistent output shapes, auto-triggered hallucination checks, and structured audit traces on every call.

Universal Entry Point

ToolDescription
couchloopIntent router — Routes any loose command to the right tool. Use for: "end session", "save this", "review code", "brainstorm this", "help me", etc.

Core Tools

ToolDescription
verifyPre-delivery verification — Catches AI hallucinations, validates packages, checks code before presenting to users
statusDashboard — Session progress, history, context window usage, protection status, preferences
conversationAI conversation with crisis detection and session memory. Actions: start, send, end, resume, status
brainstormStandalone dev thinking partner — trade-off analysis, feature design, architecture decisions
code_reviewComplete code analysis — security vulnerabilities, code smells, AI-generated errors
package_auditDependency audit — validates packages exist, checks versions, finds vulnerabilities
rememberSave and recall context, checkpoints, insights across sessions
protectFile 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's what 2 weeks of actual usage looks like:

Usage Statistics

MetricValue
Insights captured49
Active sessions5
Unique tags85+
Date rangeJan 19 - Feb 2, 2026

Development Areas Tracked

CategoryInsightsExample
🔐 Security fixes12Auth flow hardening, validation improvements
💳 Payment integration8Payment flow patterns, webhook handling
📱 Mobile development15State management, navigation guards
🗄️ Database operations6Data cleanup, schema optimization
🏗️ Architecture decisions8Caching 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 SizeRecommended Actions
Small fixsave_insight — Quick note of what was done and why
Medium featuresave_insight + save_checkpoint — Capture decisions and state
Large feature setpreserve_context + save_checkpoint + multiple save_insight — 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.

"Get my insights tagged 'auth-refactor'" → 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

Save Insights Checkpoint Session Code Review Workflow

Support

License

MIT

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