Intuition AI
Intuition AI enables powerful integrations between AI models and the Intuition knowledge graph, allowing AI systems to query, create, and interact with decentralized trust data.
What is Intuition AI?
Intuition AI bridges the gap between artificial intelligence and decentralized knowledge systems, enabling:
- Semantic Queries: AI models can query the knowledge graph using natural language
- Knowledge Creation: Programmatically create Atoms and Triples through AI analysis
- Trust Analysis: Leverage AI for intelligent signal attestation and pattern recognition
- Context Enhancement: Enrich AI responses with verified on-chain data
Model Context Protocol (MCP) Integration
The cornerstone of Intuition AI is our MCP server, which provides a standardized way for AI models to interact with the Intuition protocol through the Model Context Protocol specification.
Key Capabilities
- Extract Triples: Convert natural language into structured knowledge triples
- Search Entities: Find atoms, accounts, and concepts across the knowledge graph
- Account Information: Retrieve detailed information about accounts and their connections
- Social Graphs: Explore following/follower relationships and recommendations
- List Management: Search and manage curated lists of entities
Getting Started
Ready to integrate AI with Intuition? Check out our MCP server:
- Intuition MCP Server - Complete guide to setting up and using the MCP server for AI integrations
Use Cases
Knowledge Graph Queries
AI models can ask questions like "What are the most trusted DeFi protocols?" and receive structured data from the knowledge graph.
Automated Knowledge Creation
Transform unstructured data and natural language into structured Atoms and Triples automatically.
Trust Signal Analysis
Use AI to analyze patterns in trust signals and provide insights about entity reliability.
Enhanced AI Context
Enrich AI model responses with verified, on-chain data about entities and relationships.
Open Source
All Intuition AI components are open source and available on GitHub. The MCP server is actively maintained and welcomes community contributions.