使用说明

🌐 KnowledgeGraphMCP - Model Context Protocol Knowledge Graph System

A complete Model Context Protocol (MCP)-based system that enables LLMs to dynamically construct, query, and update knowledge graphs using Neo4j or Memgraph as the backend. Build complex knowledge graphs through natural language without hardcoding entities or relationships.

🔍 Overview

KnowledgeGraphMCP allows AI assistants to interact with graph databases in real-time, enabling:

Dynamic Entity Management: Add entities (nodes) on-the-fly with custom types and metadata
Relationship Creation: Connect entities with typed relationships
Natural Language Querying: Convert natural language to Cypher queries automatically
Graph Analytics: Get insights, statistics, and find patterns in your knowledge graph
External Data Integration: Fetch and integrate data from external APIs
Real-time Updates: Modify graph structure based on LLM interactions

✨ Features

🔄 Dynamic Graph Construction - No hardcoded schemas, create entities and relationships on demand
🧠 Natural Language Interface - Query graphs using plain English
📊 Built-in Analytics - Graph statistics, top connections, distribution analysis
🔗 External Data Integration - Pull data from APIs and automatically create graph structures
🔒 Security First - Input sanitization and safe query validation
🚀 Production Ready - Connection pooling, error handling, and graceful shutdowns
📚 Multiple Graph DBs - Supports both Neo4j and Memgraph
🎯 Domain Agnostic - Works for legal aid, startups, research, and any domain

🏗️ Architecture

┌─────────────────────────────────────────────────────────────┐
│                      LLM Client                             │
│  (Issues natural language commands)                         │
└────────────────┬────────────────────────────────────────────┘
                 │
                 │ MCP Protocol (JSON-RPC over stdio)
                 │
┌────────────────┴────────────────────────────────────────────┐
│              Knowledge Graph MCP Server                     │
│  ┌──────────────────────────────────────────────────────┐   │
│  │   Tools: addEntity, addRelationship, queryGraph,     │   │
│  │          searchEntities, graphAnalytics, etc.        │   │
│  └────────────────────┬─────────────────────────────────┘   │
│                       │                                     │
│  ┌────────────────────▼─────────────────────────────────┐   │
│  │         Database Connection Manager                  │   │
│  │           (Neo4j Driver / Memgraph)                  │   │
│  └──────────────────────────────────────────────────────┘   │
└────────────────┬────────────────────────────────────────────┘
                 │
                 │ Bolt Protocol
                 │
┌────────────────▼────────────────────────────────────────────┐
│              Neo4j / Memgraph Database                      │
│  (Graph storage with Cypher query language)                 │
└─────────────────────────────────────────────────────────────┘

📋 Prerequisites

Node.js 18+
Neo4j or Memgraph database (local or cloud)
An MCP-compatible client (Claude Desktop, Cline, etc.)

🚀 Installation

Quick Start with npx (Recommended)

Configure your MCP client:

{
  "mcpServers": {
    "knowledge-graph": {
      "command": "npx",
      "args": ["-y", "kgraph-mcp"],
      "env": {
        "NEO4J_URI": "bolt://localhost:7687",
        "NEO4J_USERNAME": "neo4j",
        "NEO4J_PASSWORD": "your_password"
      }
    }
  }
}

That's it! The server will auto-connect to your graph database.

Local Development

For contributing or modifying the code:

git clone https://github.com/gyash1512/KnowledgeGraphMCP.git
cd KnowledgeGraphMCP
npm install
npm run build

Database Setup

Using Neo4j Desktop (Easiest)

Download Neo4j Desktop
Create a new project and database
Start the database (default: bolt://localhost:7687)
Note your username and password

Using Docker

# Neo4j
docker run -d \
  --name neo4j \
  -p 7474:7474 -p 7687:7687 \
  -e NEO4J_AUTH=neo4j/your_password \
  neo4j:latest

# Or Memgraph
docker run -d \
  --name memgraph \
  -p 7687:7687 \
  memgraph/memgraph:latest

🎮 Usage

MCP Configuration

For Claude Desktop:

Edit your Claude Desktop config file:

Windows: %APPDATA%\Claude\claude_desktop_config.json
macOS: ~/Library/Application Support/Claude/claude_desktop_config.json
Linux: ~/.config/Claude/claude_desktop_config.json

{
  "mcpServers": {
    "knowledge-graph": {
      "command": "npx",
      "args": ["-y", "kgraph-mcp"],
      "env": {
        "NEO4J_URI": "bolt://localhost:7687",
        "NEO4J_USERNAME": "neo4j",
        "NEO4J_PASSWORD": "your_password"
      }
    }
  }
}

For local development:

{
  "mcpServers": {
    "knowledge-graph": {
      "command": "node",
      "args": ["./dist/server.js"],
      "cwd": "/absolute/path/to/KnowledgeGraphMCP",
      "env": {
        "NEO4J_URI": "bolt://localhost:7687",
        "NEO4J_USERNAME": "neo4j",
        "NEO4J_PASSWORD": "your_password"
      }
    }
  }
}

📚 Available Tools

1. `addEntity`

Add a new entity (node) to the knowledge graph.

Parameters:

name (string, required): Unique entity name
type (string, required): Entity type/category
metadata (object, optional): Additional properties

Example:

{
  "name": "NutriGuide AI",
  "type": "Startup",
  "metadata": {
    "founded": "2024",
    "founder": "Yash Gupta",
    "location": "Delhi",
    "industry": "HealthTech"
  }
}

2. `addRelationship`

Create a relationship between two existing entities.

Parameters:

source (string, required): Source entity name
target (string, required): Target entity name
type (string, required): Relationship type
metadata (object, optional): Relationship properties

Example:

{
  "source": "NutriGuide AI",
  "target": "Yash Gupta",
  "type": "FOUNDED_BY",
  "metadata": {
    "date": "2024-01-15",
    "role": "CEO"
  }
}

3. `queryGraph`

Query the graph using natural language or Cypher.

Parameters:

query (string, required): Natural language or Cypher query
useNaturalLanguage (boolean, optional): Auto-convert to Cypher (default: true)

Examples:

// Natural language
{
  "query": "Show all startups founded by Yash Gupta"
}

// Cypher query
{
  "query": "MATCH (s:Startup)-[:FOUNDED_BY]->(p:Person {name: 'Yash Gupta'}) RETURN s",
  "useNaturalLanguage": false
}

4. `searchEntities`

Search for entities by name or type.

Parameters:

searchTerm (string, required): Search term
entityType (string, optional): Filter by type

5. `graphAnalytics`

Get comprehensive graph statistics.

Parameters:

nodeType (string, optional): Filter by node type
relationshipType (string, optional): Filter by relationship type

Returns:

Node and relationship counts
Type distributions
Top connected entities
Graph density and average degree

6. `externalData`

Fetch data from external APIs and integrate into graph.

Parameters:

source (string, required): API source name
query (string, required): Query parameters
endpoint (string, optional): Custom API endpoint
apiKey (string, optional): API authentication

7. `deleteEntity`

Remove an entity and its relationships.

Parameters:

name (string, required): Entity name to delete

8. `getSubgraph`

Get a subgraph starting from a specific entity.

Parameters:

entityName (string, required): Starting entity
maxDepth (number, optional): Max traversal depth (default: 2)
relationshipTypes (array, optional): Filter relationship types

💡 Example Queries

Example 1: Building a Startup Ecosystem

Natural language commands:

1. "Add entity: Startup 'NutriGuide AI' with founder 'Yash Gupta'"
2. "Add entity: Investor 'Sequoia Capital' with metadata type 'VC Firm'"
3. "Add relationship: 'Sequoia Capital' invested in 'NutriGuide AI' with amount '1M USD'"
4. "Show all startups and their investors"

Expected workflow:

Creates Startup node
Creates Investor node
Creates INVESTED_IN relationship
Returns graph showing connections

Example 2: Legal Aid System

Natural language commands:

1. "Add entity: Prisoner 'John Doe' in 'Tihar Jail'"
2. "Add entity: Volunteer 'Jane Smith' specializing in 'criminal law'"
3. "Add relationship: 'Jane Smith' assigned to 'John Doe'"
4. "Show all prisoners connected to volunteers in Delhi"

Example 3: Knowledge Graph Analytics

Query:

"Get analytics for all Person entities"

Returns:

{
  "nodeCount": 150,
  "relationshipCount": 320,
  "nodeTypeDistribution": {
    "Person": 150
  },
  "topConnectedNodes": [
    {
      "name": "Yash Gupta",
      "type": "Person",
      "connectionCount": 25
    }
  ],
  "averageDegree": 4.27,
  "graphDensity": 0.0143
}

🔧 API Response Schemas

Entity Object

{
  id: string;              // Database-generated ID
  name: string;            // Entity name
  type: string;            // Entity type
  metadata: object;        // Custom properties
  createdAt: string;       // ISO timestamp
  updatedAt: string;       // ISO timestamp
}

Relationship Object

{
  id: string;              // Database-generated ID
  source: string;          // Source entity name
  target: string;          // Target entity name
  type: string;            // Relationship type
  metadata: object;        // Custom properties
  createdAt: string;       // ISO timestamp
}

Query Result

{
  nodes: Entity[];         // Matching nodes
  relationships: Relationship[];  // Matching relationships
  metadata: {
    executionTime: number; // Query time in ms
    query: string;         // Executed query
    resultCount: number;   // Number of results
  }
}

🎯 Use Cases

Startup/Investment Tracking

Map startup founders, investors, and funding rounds
Track connections between companies
Analyze investment patterns

Legal Aid Management

Connect prisoners with legal volunteers
Track case assignments and progress
Map legal case relationships

Research Networks

Build academic collaboration graphs
Track paper citations and co-authorships
Find research communities

Social Networks

Map person-to-person relationships
Analyze influence and connections
Build organizational hierarchies

Knowledge Management

Create interconnected knowledge bases
Map concepts and relationships
Build decision trees

🛠️ Development

Project Structure

KnowledgeGraphMCP/
├── src/
│   ├── server.ts        # Main MCP server
│   ├── database.ts      # Graph DB connection
│   ├── types.ts         # TypeScript types
│   ├── utils.ts         # Utility functions
│   └── cli.ts           # CLI entry point
├── dist/                # Compiled output
├── package.json
├── tsconfig.json
└── README.md

Building

npm run build        # Compile TypeScript
npm run watch        # Watch mode for development
npm run clean        # Clean dist folder

Testing

# Start the server locally
npm run start

# Or with development build
npm run dev

🔒 Security

Input Sanitization: All user inputs are sanitized
Query Validation: Dangerous Cypher operations are blocked
Connection Security: Supports encrypted connections
Environment Variables: Credentials stored securely in .env

🐛 Troubleshooting

Database Connection Failed

Verify Neo4j/Memgraph is running: http://localhost:7474
Check credentials in config
Ensure bolt port (7687) is accessible

Entity Already Exists

Entity names must be unique
Search for existing entity first
Update metadata instead of recreating

Query Returns Empty

Verify entities exist: use searchEntities
Check relationship types match
Try simpler query first

📞 Support

For issues, questions, or contributions:

Open an issue on GitHub
Check Neo4j Documentation
Review MCP Specification

🤝 Contributing

Contributions welcome! Please read CONTRIBUTING.md for guidelines.

📄 License

MIT License - see LICENSE for details.

🙏 Acknowledgments

Built with Model Context Protocol SDK
Uses Neo4j graph database
Compatible with Memgraph
Inspired by the research at ResearchMCP

Built with ❤️ using TypeScript, MCP, and Graph Databases

使用说明

🌐 KnowledgeGraphMCP - Model Context Protocol Knowledge Graph System

🔍 Overview

✨ Features

🏗️ Architecture

📋 Prerequisites

🚀 Installation

Quick Start with npx (Recommended)

Local Development

Database Setup

Using Neo4j Desktop (Easiest)

Using Docker

🎮 Usage

MCP Configuration

📚 Available Tools

1. addEntity

2. addRelationship

3. queryGraph

4. searchEntities

5. graphAnalytics

6. externalData

7. deleteEntity

8. getSubgraph

💡 Example Queries

Example 1: Building a Startup Ecosystem

Example 2: Legal Aid System

Example 3: Knowledge Graph Analytics

🔧 API Response Schemas

Entity Object

Relationship Object

Query Result

🎯 Use Cases

Startup/Investment Tracking

Legal Aid Management

Research Networks

Social Networks

Knowledge Management

🛠️ Development

Project Structure

Building

Testing

🔒 Security

🐛 Troubleshooting

Database Connection Failed

Entity Already Exists

Query Returns Empty

📞 Support

🤝 Contributing

📄 License

🙏 Acknowledgments

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自主代理操作系统

1. `addEntity`

2. `addRelationship`

3. `queryGraph`

4. `searchEntities`

5. `graphAnalytics`

6. `externalData`

7. `deleteEntity`

8. `getSubgraph`