OriginTrail DKG MCP Server (example)
About
The OriginTrail DKG MCP Server connects MCP-compatible agents with the OriginTrail Decentralized Knowledge Graph (DKG), enabling the creation, retrieval, linking, and exchange of verifiable knowledge. It is beta software and not recommended for production use.
Details
- Author
- OriginTrail
- Downloads
- 185
- Categories
- Other
Jump to
- SPARQL querying of the Decentralized Knowledge Graph.
- Natural language to structured JSON-LD knowledge asset creation.
- Decentralized, interoperable agent memory storage.
- Works with any MCP-compatible client or agentic framework.
- Supports both stdio and SSE transport modes.
Setting up with Highlight
This MCP is not yet compatible with Highlight’s one-click setup. However, you can still use it with Highlight by following these steps:
- Download and install Highlight from highlightai.com/download
- Navigate to the plugins tab and select "Add Custom Plugin"
-
Configure the plugin with the settings below
Plugin Name
OriginTrail DKG MCP Server (example)Command (node, npx, python, etc.)Please refer to the README for specific instructions on how to obtain API keys or other required environment variables.
- Enable "Start Automatically" if you want the plugin to start when Highlight launches
From the repository
Clone the repository, install dependencies with Python 3.10+ and pip install -r requirements.txt, configure environment variables (ORIGINTRAIL_NODE_URL, BLOCKCHAIN, PRIVATE_KEY, GOOGLE_API_KEY), then run python dkg_server.py --transport stdio for local clients or python dkg_server.py --transport sse for server deployment. The server exposes tools such as query_dkg_by_name and create_knowledge_asset via MCP.
Claude Desktop / Cursor
Paste into your MCP client config file to install this server.
{
"mcpServers": {
"origintrail dkg mcp server (example)": {
"dkg-mcp-server": {
"command": "python",
"args": [
"dkg_server.py",
"--transport",
"stdio"
]
}
}
}
}McpServers
{
"dkg-mcp-server": {
"command": "python",
"args": [
"dkg_server.py",
"--transport",
"stdio"
]
}
}OriginTrail DKG MCP Server (example)
Overview
The OriginTrail DKG MCP Server connects MCP-compatible agents with the OriginTrail Decentralized Knowledge Graph (DKG), making it easy to create, retrieve, link, and exchange verifiable knowledge.Note: This is BETA software and not recommended for use in production
Key Features
- SPARQL Querying: Retrieve knowledge from the DKG using flexible SPARQL queries.
- Knowledge Asset Creation: Convert natural language into structured, schema.org-compliant JSON-LD and publish it to the DKG.
- Agent Memory: Store and retrieve decentralized agent memory in a standardized, interoperable way.
- Interoperability: Works with any MCP-compatible client, including VS Code, Cursor, Microsoft Copilot agents, and more.
Getting Started
1. Clone the Repository
git clone <repo-url>
cd otdkg-mcp-server
2. Install Dependencies
Ensure you have Python 3.10+ installed. Then run:
pip install -r requirements.txt
3. Configure Environment Variables
Copy .env.example to .env and fill in the required values:
- ORIGINTRAIL_NODE_URL: You can use the default public node on testnet, use a different public testnet or mainnet node, or deploy and use your own Edge Node.
- BLOCKCHAIN: Blockchain to use for publishing Knowledge Assets on the DKG (e.g., NEUROWEB_TESTNET)
- PRIVATE_KEY: Private key of the wallet you'll use for publishing Knowledge Assets to the DKG
- GOOGLE_API_KEY: API key for Google Generative AI (you can get your API ke at https://aistudio.google.com/)
See .env.example for detailed comments and options.
4. Run the MCP Server
You can run the server in two modes:
a) Stdio Mode (for local clients like VS Code, Cursor, Claude, etc.)
python dkg_server.py --transport stdio
b) SSE Mode (for server deployment, making the DKG MCP server accessible to e.g. Microsoft Copilot Studio agents)
python dkg_server.py --transport sse
The SSE server will listen on the configured host and port (see .env).
Usage
Once the server is running, you can import it into your client and gain access to the following out-of-the-box tools:
- Query the DKG: Use the query_dkg_by_name tool to search for entities by name using SPARQL.
- Create Knowledge Assets on the DKG: Use the create_knowledge_asset tool to convert natural language into JSON-LD and publish it to the DKG.
These tools are exposed via MCP and can be invoked from any compatible agent or client.
Compatible Clients
- [x] VS Code
- [x] Cursor
- [x] Claude
- [x] Microsoft Copilot Studio agents
- [x] Any MCP-compatible LLM or agentic framework
Extending the Server
- Customize Existing Tools: Modify and enhance the existing tools in dkg_server.py or add new functionality to tailor them to your needs.
- Add New Tools: You can easily add new MCP tools by defining new functions in dkg_server.py using the @mcp.tool() decorator (e.g. a tool that will transform website URLs into knowledge on the DKG).
- Custom Prompts: Modify or add prompt templates in the prompts/ directory to customize LLM behavior.
- Contribute: Clone, enhance, and submit pull requests to add new features or tools. Community contributions are welcome!
Project Structure
- dkg_server.py — Main server and tool definitions
- prompts/ — Prompt templates for LLM-powered tools
- requirements.txt — Python dependencies
- .env.example — Example environment configuration
- origintrail-dkg-mcp.yaml — OpenAPI spec for SSE deployment
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Empower your agents to create, retrieve, and exchange verifiable knowledge on OriginTrail DKG!
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