CouchLoop EQ - MCP Server

Behavioral governance layer for safer, more consistent AI conversations.

📖 Choose Your Guide

Related MCP server: LatentContext MCP Server

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:

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:

1. Enable Developer Mode in ChatGPT Settings

2. Add as MCP connector with URL: https://mcp.couchloop.com/mcp

3. 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:

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.

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

Development Areas Tracked

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

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: support@couchloop.com

License

MIT