renoun-mcp 1.3.1

Structural observability for AI conversations and financial risk management. Detects patterns across 17 channels without analyzing content. Includes crypto risk overlay with validated drawdown reduction.

ReNoUn

Structural observability for AI conversations and financial markets

Your agent doesn't know when it's going in circles. ReNoUn does.

Detects when conversations are stuck in loops, producing cosmetic variation instead of real change, or failing to converge. Measures structural health across 17 channels without analyzing content — works on any turn-based interaction.

Why?

LLMs get stuck. They produce responses that sound different but are structurally identical — what we call surface variation. A human might notice after 5 turns. An agent never will.

ReNoUn catches this in ~200ms by measuring structure, not content. It works on any language, any topic, any model.

Install

pip install renoun-mcp

For financial market analysis with streaming support:

pip install renoun-mcp[finance]

Quick Start

As an MCP Server (Claude Desktop)

Add to your Claude Desktop config (~/Library/Application Support/Claude/claude_desktop_config.json):

{
    "mcpServers": {
        "renoun": {
            "command": "python3",
            "args": ["-m", "server"],
            "env": {
                "RENOUN_API_KEY": "rn_live_your_key_here"
            }
        }
    }
}

As a REST API

curl -X POST https://web-production-817e2.up.railway.app/v1/analyze \
  -H "Authorization: Bearer rn_live_your_key_here" \
  -H "Content-Type: application/json" \
  -d '{"utterances": [
    {"speaker": "user", "text": "I feel stuck"},
    {"speaker": "assistant", "text": "Tell me more about that"},
    {"speaker": "user", "text": "I keep going in circles"},
    {"speaker": "assistant", "text": "What patterns do you notice?"},
    {"speaker": "user", "text": "The same thoughts repeat"}
  ]}'

As a Claude Code MCP

claude mcp add renoun python3 -m server

Demo Output

{
  "dialectical_health": 0.491,
  "loop_strength": 0.36,
  "channels": {
    "recurrence": { "Re1_lexical": 0.0, "Re2_syntactic": 0.3, "Re3_rhythmic": 0.5, "Re4_turn_taking": 1.0, "Re5_self_interruption": 0.0, "aggregate": 0.36 },
    "novelty":    { "No1_lexical": 1.0, "No2_syntactic": 1.0, "No3_rhythmic": 0.5, "No4_turn_taking": 0.5, "No5_self_interruption": 0.0, "No6_vocabulary_rarity": 0.833, "aggregate": 0.639 },
    "unity":      { "Un1_lexical": 0.5, "Un2_syntactic": 0.135, "Un3_rhythmic": 0.898, "Un4_interactional": 0.7, "Un5_anaphoric": 0.705, "Un6_structural_symmetry": 0.5, "aggregate": 0.573 }
  },
  "constellations": [],
  "novelty_items": [
    { "index": 4, "text": "The same thoughts repeat", "score": 0.457, "reason": "shifts conversational direction" }
  ],
  "summary": "Moderate dialectical health (DHS: 0.491). Diverse exploration (loop strength: 0.36). Key moment at turn 4.",
  "recommendations": ["■ Key novelty at turn 4. Consider returning to this moment."]
}

Tools

Tool	Purpose	Speed	Tier
renoun_analyze	Full 17-channel structural analysis with breakthrough detection	~200ms	Pro
renoun_health_check	Quick triage — one score, one pattern, one action	~50ms	Free
renoun_compare	Structural A/B test between two conversations	~400ms	Pro
renoun_pattern_query	Save, query, and trend longitudinal session history	~10ms	Pro
renoun_finance_analyze	Structural analysis of OHLCV data with exposure recommendations	~200ms	Pro

How It Works

ReNoUn measures 17 structural channels across three dimensions:

Recurrence (5 channels) — Is structure repeating? Lexical, syntactic, rhythmic, turn-taking, and self-interruption patterns.

Novelty (6 channels) — Is anything genuinely new emerging? Lexical novelty, syntactic novelty, rhythmic shifts, turn-taking changes, self-interruption breaks, and vocabulary rarity.

Unity (6 channels) — Is the conversation holding together? Lexical coherence, syntactic coherence, rhythmic coherence, interactional alignment, anaphoric reference, and structural symmetry.

From these 17 signals, ReNoUn computes a Dialectical Health Score (DHS: 0.0–1.0) and detects 8 constellation patterns, each with a recommended agent action:

Pattern	What It Means	Agent Action
CLOSED_LOOP	Stuck recycling the same structure	explore_new_angle
HIGH_SYMMETRY	Rigid, overly balanced exchange	introduce_variation
CONVERGENCE	Moving toward resolution	maintain_trajectory
PATTERN_BREAK	Something just shifted	support_integration
SURFACE_VARIATION	Sounds different but structurally identical	go_deeper
SCATTERING	Falling apart, losing coherence	provide_structure
REPEATED_DISRUPTION	Keeps breaking without stabilizing	slow_down
DIP_AND_RECOVERY	Disrupted then recovered	acknowledge_shift

Pricing

	Free	Pro ($4.99/mo)
renoun_health_check	✓	✓
renoun_analyze	—	✓
renoun_compare	—	✓
renoun_pattern_query	—	✓
renoun_finance_analyze	—	✓
Daily requests	20	1,000
Max turns per analysis	200	500

Get your API key: Subscribe via Stripe or visit harrisoncollab.com.

REST API

Base URL: https://web-production-817e2.up.railway.app

Endpoint	Method	Auth	Description
/v1/analyze	POST	Bearer	Full 17-channel analysis
/v1/health-check	POST	Bearer	Fast structural triage
/v1/compare	POST	Bearer	A/B test two conversations
/v1/patterns/{action}	POST	Bearer	Longitudinal pattern history
/v1/finance/analyze	POST	Bearer	OHLCV structural analysis with exposure recs
/v1/status	GET	None	Liveness + version info
/v1/billing/checkout	POST	None	Create Stripe checkout session
/docs	GET	None	Interactive API explorer

All authenticated endpoints require: Authorization: Bearer rn_live_...

Input Format

All analysis tools accept conversation turns as speaker/text pairs:

{
    "utterances": [
        {"speaker": "user", "text": "I keep going back and forth on this decision."},
        {"speaker": "assistant", "text": "What makes it feel difficult to commit?"},
        {"speaker": "user", "text": "I think I'm afraid of making the wrong choice."}
    ]
}

Minimum 3 turns required. 10+ recommended for reliable results. 20+ for stable constellation detection.

Integration

Claude Desktop

{
    "mcpServers": {
        "renoun": {
            "command": "python3",
            "args": ["-m", "server"],
            "env": { "RENOUN_API_KEY": "rn_live_your_key_here" }
        }
    }
}

Claude Code

RENOUN_API_KEY=rn_live_your_key_here claude mcp add renoun python3 -m server

Generic MCP Client

{
    "transport": "stdio",
    "command": "python3",
    "args": ["-m", "server"],
    "env": { "RENOUN_API_KEY": "rn_live_your_key_here" }
}

Environment Variable

export RENOUN_API_KEY=rn_live_your_key_here

Longitudinal Storage

Results persist to ~/.renoun/history/. Use renoun_pattern_query to save, list, query, and trend session history over time. Filter by date, domain, constellation pattern, or DHS threshold.

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Financial Risk Overlay

The same 17-channel engine that detects stuck conversations also detects structural disorder in financial markets. When market structure breaks down, reduce exposure. When it's coherent, stay the course.

• 31/31 drawdown reduction across 9 crypto assets and 5 timeframes
• 21.3pp average DD improvement on black swan events (COVID, LUNA, FTX)
• 0.1 Sharpe median cost — cheap insurance
• Works on any OHLCV data — crypto, equities, forex, commodities
• Not a prediction engine — a structural risk overlay

How It Works (Finance)

ReNoUn maps OHLCV candle data onto the same 17 structural channels used for conversation analysis:

Recurrence (Re1-Re5) — Is the market repeating known patterns? Price action rhythms, volume profiles, volatility persistence, session structure, and mean-reversion signatures.

Novelty (No1-No6) — Is something genuinely new happening? Regime breaks, flow reversals, volatility spikes, session disruptions, behavioral shifts, and cross-signal rarity.

Unity (Un1-Un6) — Is the market holding together? Trend cohesion across price and volume, volatility-price alignment, session continuity, reference-frame stability, and structural symmetry between first-half and second-half of the analysis window.

From these 17 signals, the engine produces:

• DHS (0.0-1.0) — Dialectical Health Score. High = coherent structure, low = disorder.
• Constellation patterns — The same 8 patterns (CONVERGENCE, SCATTERING, CLOSED_LOOP, etc.) applied to market structure.
• Stress metrics — Drawdown depth, volatility expansion, and structural fragility indicators.
• Exposure scalar — A smoothed, persistence-weighted recommendation mapping structural health to position sizing. High DHS = full exposure, low DHS = reduce.

Quick Start (Finance)

Python API

from renoun_finance import analyze_financial

klines = [
    {"open": 100, "high": 105, "low": 98, "close": 103, "volume": 1000},
    {"open": 103, "high": 107, "low": 101, "close": 106, "volume": 1200},
    # ... 50+ candles recommended for reliable signals
]

result = analyze_financial(klines, symbol="BTCUSDT", timeframe="1h")

print(result["dialectical_health"])  # 0.72
print(result["constellations"])      # [{"detected": "CONVERGENCE", ...}]
print(result["stress"])              # {"drawdown": 0.15, "vol_expansion": 0.08}
print(result["exposure"])            # {"scalar": 0.85, "regime": "healthy"}

MCP Tool

{
    "tool": "renoun_finance_analyze",
    "arguments": {
        "klines": [
            {"open": 100, "high": 105, "low": 98, "close": 103, "volume": 1000},
            {"open": 103, "high": 107, "low": 101, "close": 106, "volume": 1200}
        ],
        "symbol": "BTCUSDT",
        "timeframe": "1h",
        "include_exposure": true
    }
}

REST API

curl -X POST https://web-production-817e2.up.railway.app/v1/finance/analyze \
  -H "Authorization: Bearer rn_live_your_key_here" \
  -H "Content-Type: application/json" \
  -d '{
    "klines": [{"open": 100, "high": 105, "low": 98, "close": 103, "volume": 1000}],
    "symbol": "BTCUSDT",
    "timeframe": "1h",
    "include_exposure": true
  }'

Live Streaming

For real-time monitoring with continuous exposure updates:

python renoun_stream.py --symbol BTCUSDT --tf 5m
python renoun_stream.py --symbols BTCUSDT,ETHUSDT,SOLUSDT  # multi-asset

The streamer connects to exchange websocket feeds, buffers candles, and runs renoun_finance_analyze on a rolling window. Output includes live DHS, constellation detection, and exposure scalar updates.

Supported timeframes: 1m, 5m, 15m, 1h, 4h, 1d.

Requires the finance extras: pip install renoun-mcp[finance].

Backtest Results

Validated across 31 datasets (9 crypto assets, 5 timeframes per asset):

Metric	Result
Datasets tested	31 (9 assets x 5 timeframes)
Drawdown improved	31/31 (100%)
Avg DD improvement	5.7pp
Black swan DD improvement	21.3pp avg (4 events)
Sharpe improved	14/31 (45%)
Sharpe degraded	17/31 (55%)
Median Sharpe cost	0.1
Negative impact (DD worse)	0/31 (0%)

The pattern is consistent: ReNoUn reduces drawdowns in every tested configuration. Sharpe impact is mixed because the overlay occasionally reduces exposure during recoveries, trimming upside along with downside. The median Sharpe cost of 0.1 is the price of insurance — you give up a small amount of return for significantly better tail-risk protection.

Best on high-volatility assets (DOGE, SHIB, ETH) where structural disorder is most frequent and drawdown events are deepest.

The Honest Take

ReNoUn is not a prediction engine. It does not generate alpha. It does not tell you what to buy or when to enter.

What it does: measures structural market disorder and reduces exposure when structure breaks down. When price, volume, and volatility signals lose coherence — when the 17 channels show SCATTERING or REPEATED_DISRUPTION — the exposure scalar pulls you back. When structure is healthy and converging, you stay fully allocated.

Think of it as a VIX-based position sizer for markets where there is no options-implied volatility. Crypto has no VIX. Most small-cap equities have no liquid options chain. ReNoUn fills that gap by deriving structural disorder directly from OHLCV data.

Best used as a risk overlay — pair it with your own signal, your own strategy, your own edge. ReNoUn handles the "when to reduce" question so your signal can focus on the "what to trade" question.

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Version

• Server: 1.2.0
• Engine: 4.1
• Schema: 1.1
• Protocol: MCP 2024-11-05

Related

The ReNoUn Cowork Plugin provides skill files, slash commands, and reference documentation for agents using the Cowork plugin system. The MCP server and plugin share the same engine and can be used independently or together.

Patent Notice

The core computation engine is proprietary and patent-pending (#63/923,592). This MCP server wraps it as a black box. Agents call engine.score() and receive structured results — they never access internal algorithms.

License

MCP server and API wrapper: MIT. Core engine: Proprietary.

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Harrison Collab · API Docs · PyPI