mcp-knowledge-graph MCP Server: Persistent Memory Through Knowledge Graphs in AI Applications

Overview: What is mcp-knowledge-graph MCP Server?

The mcp-knowledge-graph MCP server represents an enhanced implementation of persistent memory for AI applications, built on the Model Context Protocol (MCP). This server facilitates the storage and retrieval of user-specific information across multiple interactions, enabling a more personalized and context-aware experience for users engaging with AI systems. By utilizing a local knowledge graph with customizable --memory-path, this server goes beyond ephemeral storage methods such as npx installations.

🔧 Core Features & MCP Capabilities

Entities, Relations, & Observations

The core feature of the mcp-knowledge-graph MCP server lies in its robust data model. Nodes are defined as entities, each with a unique identifier, predefined entity type, and associated observations:

• Entities: Represent real-world individuals or concepts.
• Relations: Link between entities using relation types described in active voice.
• Observations: Discrete pieces of information attached to specific entities.

For example:

{
  "name": "John_Smith",
  "entityType": "person",
  "observations": ["Speaks fluent Spanish", "Graduated in 2019"]
}

Relations are stored as directed connections between these nodes, illustrating interactions or attributes:

• Relation Example:

{
  "from": "John_Smith",
  "to": "ExampleCorp",
  "relationType": "works_at"
}

mcp-knowledge-graph supports multiple operations on this graph through API tools including create_entities, create_relations, and add_observations.

⚙️ MCP Architecture & Protocol Implementation

API Tools Overview

The primary tools provided by the mcp-knowledge-graph MCP server include:

• create_entities: Creating new entities with associated observations.
• create_relations: Establishing relations between existing entities.
• add_observations: Adding novel information to existing entities.
• delete_entities: Removing selected entities and their related relations.
• delete_observations: Removing specific pieces of observed information from an entity.
• read_graph: Reading the entire knowledge graph structure.
• search_nodes: Searching nodes based on various criteria.
• open_nodes: Retrieving detailed information about specified nodes.

MCP Protocol Flow Diagram

For visualizing how AI applications interact with the mcp-knowledge-graph server, here’s a flow diagram:

graph TB
    A[AI Application] -->|MCP Client| B[MCP Protocol]
    B --> C[MCP Server]
    C --> D[Knowledge Graph Database]
    style A fill:#e1f5fe
    style C fill:#f3e5f5
    style D fill:#e8f5e8

This diagram illustrates the flow from an AI application making requests via MCP, to protocol level validation and data retrieval or modification against a local knowledge graph.

🚀 Getting Started with Installation

Setup with Claude Desktop

To integrate mcp-knowledge-graph into your Claude Desktop configuration, include this in your claude_desktop_config.json:

{
  "mcpServers": {
    "[server-name]": {
      "command": "npx",
      "args": ["-y", "@modelcontextprotocol/server-[name]"]
    }
  }
}

Custom Memory Path Configuration

For more extensive customization, specify a custom memory path using:

{
  "mcpServers": {
    "[server-name]": {
      "command": "npx",
      "args": ["-y", "@modelcontextprotocol/server-[name]", "--memory-path", "/path/to/your/memory.jsonl"]
    }
  }
}

💡 Key Use Cases in AI Workflows

Chatbot Personalization

Imagine a customer service chatbot that uses mcp-knowledge-graph to build user profiles as interactions unfold. Based on historical data, the server can predict preferences and tailor responses dynamically.

Recommendation System Enhancement

A recommendation engine could leverage user-specific data stored within the knowledge graph for more accurate recommendations based on past behaviors and preferences.

🔌 Integration with MCP Clients

Compatibility matrix highlighting supported MCP clients:

MCP Client	Resources	Tools	Prompts
Claude Desktop	✅	✅	✅
Continue	✅	✅	✅
Cursor	❌	✅	❌

📊 Performance & Compatibility Matrix

This matrix outlines the compatibility and performance metrics of mcp-knowledge-graph across various AI platforms:

Client	Resources	Tools	Prompts	Status
Claude Desktop	Full Support	✅	✅	Fully Compatible
Continue	Full Support	✅	✅	Fully Compatible
Cursor	Tool Only	✅	Not Supported	Partially Supported

🛠️ Advanced Configuration & Security

Customization Example

Customize the system prompt for users interacting with your AI model:

Follow these steps for each interaction:
1. User Identification: Assume all interactions are with default_user until information proves otherwise.
2. Memory Retrieval: Always start by saying "Remembering..." and retrieving relevant data from memory.
3. Memory Gathering: Take note of important details like identity, behaviors, preferences, and goals during conversations.
4. Memory Update: Regularly update the knowledge graph based on new insights.

Security Considerations

Ensure secure handling of persistent storage paths by setting environment variables or configuring default paths carefully.

❓ Frequently Asked Questions (FAQ)

1. Can I use different MCP servers with mcp-knowledge-graph?

   • Yes, mcp-knowledge-graph supports integration with various other MCP servers through configuration parameters and custom memory paths.

2. How does the server handle multiple concurrent users?

   • The knowledge graph mechanism allows for distinct user profiles to be managed simultaneously without conflicts.

3. Does this work on mobile devices?

   • While primarily designed for desktop setups, the architecture is modular enough to be adapted to more resource-constrained environments like mobile apps with some adjustments.

4. How can I optimize performance for large knowledge graphs?

   • Implement indexing strategies and caching mechanisms to ensure fast query response times even under heavy loads.

5. Are there any limitations on entity types or relation types?

   • Customization is possible; users can define additional entity types and relation templates according to specific application needs.

👨‍💻 Development & Contribution Guidelines

Contributions are highly encouraged! To get started, follow these steps:

1. Ensure you have Node.js installed.
2. Clone the mcp-knowledge-graph repository from GitHub.
3. Navigate into the project directory and install dependencies via npm.
4. Develop your features by adhering to existing coding standards and documenting changes clearly.

🌐 MCP Ecosystem & Resources

Explore further into the broader MCP ecosystem, including additional resources and community support channels:

Recommended Reading

• Official Model Context Protocol documentation: [link]
• Community forums and Slack channels for active developers and users.

By leveraging mcp-knowledge-graph with your AI applications, you can achieve deeper contextual understanding and more nuanced interaction patterns, truly enhancing the user experience in smart, adaptive systems.