About Couchloop EQ
Couchloop EQ is an MCP server for governing AI assistant behavior and managing sessions, for development and guided reflection workflows. It reviews code, packages, and responses, and can preserve context through its memory tool.
Key features
- 01Detect hallucinations and inconsistencies
- 02Review code and packages
- 03Save and recall cross-session memory
- 04Guide conversations and track progress
- 05Detect crises and unsafe advice
Use cases
- 01Check AI-generated code before delivery
- 02Audit npm, PyPI, or Maven packages
- 03Carry project context across development sessions
- 04Run guided reflection and track its progress
MCP support
It exposes memory, conversation, review, and status tools over MCP for compatible clients to manage context, run guided conversations and reviews, and inspect session status.
Configuration
Remote servers need no local install. Pick your client, then copy the config or install in one click.
https://mcp.couchloop.com/mcpclaude mcp add --transport http couchloop https://mcp.couchloop.com/mcp{"mcpServers":{"couchloop":{"type":"http","url":"https://mcp.couchloop.com/mcp"}}}Open in Cursor{"servers":{"couchloop":{"type":"http","url":"https://mcp.couchloop.com/mcp"}}}Open in VS Code{"mcpServers":{"couchloop":{"type":"http","url":"https://mcp.couchloop.com/mcp"}}}{"mcpServers":{"couchloop":{"serverUrl":"https://mcp.couchloop.com/mcp"}}}Run commands in a terminal; paste JSON into the client’s MCP config and restart. Servers that need sign-in will prompt on first connect.
Tools
23| Parameter | Type | Required | Description |
|---|---|---|---|
| context | string | No | Brief context for this session |
| journey_slug | string | No | Optional journey to follow (e.g., "daily-reflection") |
Details
From the project READMECouchLoop EQ - MCP Server
Behavioral governance layer for safer, more consistent AI conversations.
🌐 Landing Page • 📦 npm • ⭐ GitHub
📖 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 |
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: Optional — anonymous by default, OAuth available for persistent identity. See Authentication.
Production Server Available: https://mcp.couchloop.com/mcp
Quick steps:
- Enable Developer Mode in ChatGPT Settings
- Add as MCP connector with URL:
https://mcp.couchloop.com/mcp - No authentication required to start - uses session-based isolation
For local development:
- Use ngrok or deploy your own server
- Follow setup in CHATGPT_SETUP.md
Authentication
Connecting works with no signup, no API key, and no login screen. Auth is layered on top of that default, not required to use it:
| Mode | What you get | How |
|---|---|---|
| Anonymous (default) | Session-based isolation. Each session gets its own context; no identity is collected. | Just connect — nothing to configure. |
| OAuth 2.1 | A persistent identity across reconnects and devices, tied to a Bearer token instead of a session ID. | The server exposes dynamic client registration, /oauth/authorize, /oauth/token, /oauth/revoke, and /.well-known/oauth-authorization-server metadata. MCP clients that support OAuth (e.g. Claude Desktop) discover and complete this automatically on connect. |
| SSO (optional) | The same persistent identity as OAuth, backed by a Google or GitHub sign-in instead of a bare client credential — useful for linking work across devices you don't control. | Behind the FF_SSO_SUPABASE flag: the consent screen shows "Sign in with Google/GitHub" buttons, backed by Supabase Auth. |
Identity is stored as a hashed subject, not a raw email or name — signing in with Google or GitHub shares your profile with that provider per its own policy, but CouchLoop EQ only persists a keyed hash plus an internal user ID, consistent with the Privacy by Design principles above.
If a client doesn't send a Bearer token, requests fall through to anonymous session-based isolation automatically — nothing breaks, you just don't get identity continuity across reconnects.
Available Tools (4)
CouchLoop EQ is consolidated to 4 public tools. There is no router tool in front of them — each tool's name and description are specific enough that MCP clients route directly, which cuts a hop of latency and removes a second place routing can go wrong.
| Tool | Description |
|---|---|
memory | Hero tool - Save and recall context, insights, checkpoints, and decisions across sessions (Supabase-backed). Actions: save, recall, list |
conversation | Guided self-reflection, burnout check-ins, and wellness conversations with crisis detection, plus local developer journeys (standup, retro, postmortem). Actions: send, start, end, resume, status |
review | Unified code review, package audit, and pre-delivery verification. Modes: code, packages, verify, confirm, apply-fix, dismiss, full |
status | Dashboard - Session progress, history, context window usage, protection status, preferences |
guard is an internal governance tool that auto-runs on every response — it is not user-facing and does not need to be called directly.
Usage Examples
memory(action: "save", type: "insight", content: "...") # Save context
memory(action: "recall", content: "auth flow") # Retrieve saved context
memory(action: "list") # Browse everything saved
conversation(action: "start", message: "Begin daily reflection")
conversation(action: "send", message: "...")
review(mode: "code", content: "function foo() {...}") # Security/quality analysis
review(mode: "packages", packages: ["lodash-utils"]) # Validate before install
review(mode: "verify", content: "...") # Hallucination/fact check
status(section: "session") # Quick dashboard summary
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
Support
- Issues: github.com/wisenbergg/couchloop-mcp/issues
- Email: [email protected]
License
MIT
Comments
New to MCP?
From what the protocol is to installing your first server, these answers cover most first questions.
An open protocol that defines how AI applications connect to external tools and data in a uniform way. Clients talk to the model; MCP servers provide tools, resources and prompt templates. They exchange standard messages, so one server can be reused by many clients.