Skip to main content
Glama

EPH-MCP: Emergent Pattern Hunter

by psikosen

🕸️ EPH-MCP: Emergent Pattern Hunter

A revolutionary thinking architecture for LLMs via MCP (Model Context Protocol)

EPH-MCP transforms how AI systems reason by simulating the emergence of insights from interacting thought fragments, similar to how patterns arise in complex physical systems.

Key Features

  • Bottom-up Insight Emergence: Instead of forcing conclusions, the insights just show up once all the pieces bounce around enough.

  • Quantum-like Thought Dynamics: Ideas overlap, collide, and stick together—sometimes they’re in two states at once until the picture clears.

  • Multi-scale Pattern Detection: We can spot the small stuff and the big picture at the same time—like zooming from street level to skyline.

  • Contradiction as Feature: Tension isn’t a bug, it’s fuel. Conflicts push the thinking somewhere new.

  • Field-based Reasoning: Everything plays out in this high-dimensional “idea space,” where concepts pull, push, and interact like a living grid.

🚀 Quick Start

Installation

# Clone the repository git clone https://github.com/yourusername/eph-mcp.git cd eph-mcp # Install dependencies pip install -r requirements.txt python -m spacy download en_core_web_sm # Quick test python quickstart.py

Basic Usage

Start MCP Server

python -m eph_mcp.server

The server will start on localhost:3333 by default.

How It Works

EPH uses a 5-phase process:

Phase 1: Thought Explosion

First we blow up the question into a bunch of little sparks—50 to 150 fragments, each one a different angle or half-formed idea.
We mix in every trick we’ve got: free association, “what if” games, parallel universes, quantum superposition vibes.
Each fragment lands in some wild high-dimensional space, like confetti drifting around a cosmic dance floor.

Phase 2: Interaction Dynamics

Now those fragments start bumping into each other like charged particles.

  • Similar ones pull together.

  • Opposites push apart.

  • Some bind tightly, others spin off.

It’s basically like running a mini-universe simulation where ideas collide until the system chills into something stable (simulated annealing).

Phase 3: Pattern Detection

From the chaos, we spot emergent shapes—like finding constellations in the stars:

  • Crystalline lattices → clean, regular structures

  • Strange attractors → looping chaos

  • Phase transitions → that “sudden click” when ideas reorganize

  • Soliton waves → insights that keep traveling without losing shape

  • …plus more funky forms

Phase 4: Pattern Crystallization

Here, the raw patterns solidify into actual insights.
We check each one for:

  • Confidence (does it hold up?)

  • Novelty (is it fresh?)

  • Clarity (can you actually explain it to a friend?)

We don’t force everything to agree—contradictions are saved too, like tension in a good story.

Phase 5: Pattern Weaving

Finally, we stitch the insights together into something you can actually use.
Different ways to weave:

  • Convergent synthesis → pull it all into one neat answer

  • Dialectical → thesis + antithesis → synthesis

  • Narrative threading → tell it like a story, connecting the dots naturally

📊 Configuration

Create a config.json file to customize behavior:

{ "explosion": { "n_fragments": 100, "temperature": 1.5, "embedding_model": "all-MiniLM-L6-v2" }, "interaction": { "iterations": 150, "initial_temperature": 1.0, "cooling_rate": 0.995 }, "detection": { "min_pattern_size": 3, "pattern_threshold": 0.5 }, "crystallization": { "confidence_threshold": 0.5, "novelty_threshold": 0.3 }, "weaving": { "max_insights": 5, "coherence_threshold": 0.6 } }

🛠️ MCP Tools

The server exposes 4 main tools via MCP:

think_emergently

Main reasoning tool - applies full EPH process

{ "query": "Your question here", "return_intermediate": false, "visualize": true }

analyze_patterns

Analyze text for emergent patterns without full reasoning

{ "text": "Text to analyze", "pattern_types": ["contradiction", "harmony"], "min_confidence": 0.5 }

compare_thoughts

Compare multiple ideas for relationships

{ "thoughts": ["idea 1", "idea 2", "idea 3"], "find_contradictions": true, "find_harmonies": true }

reasoning_history

Access and analyze past reasoning sessions

{ "last_n": 5, "analyze": true }

Enable with visualization.enabled: true in config.

Testing

Run the test suite:

# Basic tests python tests/test_basic.py # Full test suite (if available) pytest tests/

📚 Examples

Explore different reasoning scenarios:

python examples/usage_examples.py

Contributing

Contributions are welcome! Areas of interest:

  • New generation strategies for thought explosion

  • Alternative pattern detection algorithms

  • Visualization improvements

  • Performance optimization

  • Integration with other MCP tools

Acknowledgments

  • Inspired by physics and emergent systems

"In the dance of fragments, meaning emerges" - EPH Philosophy

-
security - not tested
F
license - not found
-
quality - not tested

MCP directory API

We provide all the information about MCP servers via our MCP API.

curl -X GET 'https://glama.ai/api/mcp/v1/servers/psikosen/eph_mcp'

If you have feedback or need assistance with the MCP directory API, please join our Discord server