🧠 Project Synapse MCP Server

Autonomous Knowledge Synthesis and Inference Engine

Project Synapse is a revolutionary MCP (Model Context Protocol) server that transforms raw text into interconnected knowledge graphs and autonomously generates insights through advanced pattern detection. It combines formal semantic analysis (Montague Grammar) with Zettelkasten methodology to create a true cognitive partnership with AI.

🌟 Key Features

🔬 Semantic Blueprint (Montague Grammar)

• Formal semantic analysis for precise meaning extraction

• Compositional semantics with lambda calculus

• Logical form generation from natural language

• Ambiguity resolution through rule-based frameworks

🕸️ Knowledge Cortex (Neo4j Graph Database)

• Interconnected storage of entities, relationships, and facts

• High-performance graph traversal and pattern detection

• Scalable architecture supporting complex queries

• Provenance tracking for all knowledge elements

🧮 Autonomous Zettelkasten Engine

• Pattern detection using graph algorithms and ML

• Autonomous insight generation with confidence scoring

• Auditable reasoning trails for all hypotheses

• Continuous learning and knowledge synthesis

🔄 MCP Integration

• Full MCP protocol compliance for LLM integration

• Rich tool set for knowledge manipulation

• Real-time resources for knowledge statistics

• Guided prompts for semantic analysis workflows

🚀 Quick Start

Prerequisites

• Python 3.10+

• Neo4j Database

• uv package manager (recommended)

Installation

1. Clone and setup project:

2. Setup Neo4j Database:

3. Download spaCy model:

4. Configure environment:

Claude Desktop Integration

Add the following to your Claude Desktop configuration file (~/Library/Application Support/Claude/claude_desktop_config.json on macOS):

🛠️ Core Tools

ingest_text

Process and analyze text using the full semantic pipeline:

generate_insights

Trigger autonomous insight generation:

• Pattern detection using graph algorithms

• Community detection and centrality analysis

• Semantic clustering and path analysis

• Confidence-scored hypothesis generation

query_knowledge

Natural language querying with insight-first responses:

• Prioritizes synthesized insights over raw facts

• Provides complete reasoning trails

• Supports complex semantic queries

explore_connections

Graph traversal for discovering hidden relationships:

• Multi-hop connection exploration

• Unexpected pathway identification

• Relationship strength analysis

analyze_semantic_structure

Deep semantic analysis using Montague Grammar:

• Logical form generation

• Entity-relationship extraction

• Truth-conditional semantics

• Compositional meaning analysis

📊 Resources

synapse://knowledge_stats

Real-time knowledge graph statistics:

• Entity and relationship counts

• Insight generation metrics

• Processing performance data

• System health indicators

synapse://insights/{topic}

Topic-specific insight retrieval:

• All insights related to a topic

• Evidence trails and confidence scores

• Pattern type classification

• Chronological insight development

🎯 Prompts

Knowledge Synthesis Prompt

Structured prompt for comprehensive topic analysis using formal semantic reasoning and Zettelkasten methodology.

Semantic Analysis Prompt

Multi-turn conversation template for deep Montague Grammar-based semantic analysis.

Insight Validation Prompt

Systematic validation of AI-generated insights against evidence and logical consistency.

🧭 Architecture

Components

1. Semantic Blueprint: Montague Grammar parser for formal meaning analysis

2. Knowledge Cortex: Neo4j graph database for interconnected storage

3. Zettelkasten Engine: Autonomous pattern detection and insight synthesis

4. MCP Interface: Protocol-compliant integration with LLM applications

🔧 Configuration

Environment Variables

See .env.example for complete configuration options:

• Database: Neo4j connection settings

• AI Models: API keys for various providers

• Processing: Batch sizes and thresholds

• Insight Generation: Confidence levels and intervals

Performance Tuning

• Adjust SEMANTIC_BATCH_SIZE for processing throughput

• Configure PATTERN_DETECTION_INTERVAL for insight frequency

• Set INSIGHT_CONFIDENCE_THRESHOLD for quality control

🧪 Development

Running Tests

Development Server

Code Quality

📚 Theoretical Foundation

Montague Grammar

• Formal compositional semantics

• Lambda calculus for meaning representation

• Model-theoretic truth conditions

• Systematic syntax-semantics correspondence

Zettelkasten Method

• Atomic knowledge units with unique identifiers

• Explicit linking for knowledge networks

• Emergent structure through bottom-up organization

• Continuous expansion and connection building

Graph Theory

• Community detection for knowledge clustering

• Centrality analysis for importance ranking

• Path analysis for connection discovery

• Network topology for insight generation

🤝 Contributing

1. Fork the repository

2. Create a feature branch (git checkout -b feature/amazing-feature)

3. Commit your changes (git commit -m 'Add amazing feature')

4. Push to the branch (git push origin feature/amazing-feature)

5. Open a Pull Request

Development Guidelines

• Follow PEP 8 and use type hints

• Write comprehensive docstrings

• Include tests for new functionality

• Update documentation for changes

📄 License

This project is licensed under the MIT License - see the LICENSE file for details.

🙏 Acknowledgments

• Montague Grammar foundational work by Richard Montague

• Zettelkasten methodology inspired by Niklas Luhmann

• MCP protocol by Anthropic for LLM integration

• Neo4j for graph database excellence

🔮 Roadmap

• Multi-modal processing (images, documents)

• Real-time collaborative knowledge building

• Advanced NLP beyond Montague Grammar

• Integration with external knowledge bases

• Mobile and web interfaces

• Enterprise security features

Project Synapse: Transforming AI from reactive information retrieval to proactive cognitive partnership.