How do I use RDF4J MCP Server?

1. Click on "Install Server". 2. Wait a few minutes for the server to deploy. Once ready, it will show a "Started" state. 3. In the chat, type @ followed by the MCP server name and your instructions, e.g., "@RDF4J MCP Server Show me a summary of the classes and properties in this knowledge graph" That's it! The server will respond to your query, and you can continue using it as needed. Here is a step-by-step guide with screenshots.

RDF4J MCP Server

Explore knowledge graphs with Claude - A Model Context Protocol (MCP) server that enables AI-powered exploration of RDF data and SPARQL querying.

Quick Demo

Try it out in under 2 minutes:

# 1. Clone and install
git clone https://github.com/your-org/rdf4j-mcp.git
cd rdf4j-mcp
uv sync  # or: pip install -e .

# 2. Start RDF4J + load sample data
./examples/setup-demo.sh

# 3a. Run as local MCP server (stdio)
rdf4j-mcp --server-url http://localhost:8081/rdf4j-server --repository demo

# 3b. Or run as remote HTTP server
rdf4j-mcp-server --port 3000 --server-url http://localhost:8081/rdf4j-server --repository demo

Then try these prompts with Claude:

"What classes and properties are in this knowledge graph?"

"Find all people and the projects they work on"

"Show me the project with the highest budget"

Installation

Prerequisites: Python 3.11+, Docker (for RDF4J)

git clone https://github.com/your-org/rdf4j-mcp.git
cd rdf4j-mcp
uv sync  # or: pip install -e .

Two Ways to Run

Command	Transport	Use Case
`rdf4j-mcp`	stdio	MCP client spawns locally (Claude Desktop, VS Code)
`rdf4j-mcp-server`	HTTP/SSE	Standalone remote server, multiple clients

Option 1: Local Mode (stdio)

The MCP client spawns the server as a local process:

rdf4j-mcp --server-url http://localhost:8080/rdf4j-server --repository my-repo

Option 2: Remote Mode (HTTP/SSE)

Run as a standalone HTTP server:

# Start the server
rdf4j-mcp-server --port 3000 \
  --server-url http://localhost:8080/rdf4j-server \
  --repository my-repo

# Or with environment variables
export RDF4J_MCP_RDF4J_SERVER_URL=http://localhost:8080/rdf4j-server
export RDF4J_MCP_DEFAULT_REPOSITORY=my-repo
rdf4j-mcp-server --port 3000

# Or with uvicorn (production)
uvicorn rdf4j_mcp.main:app --host 0.0.0.0 --port 3000 --workers 4

Endpoints:

GET /sse - SSE endpoint for MCP clients
GET /health - Health check
GET /info - Server configuration

Client Configuration

Claude Desktop

Config file locations:

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

Local mode (stdio):

{
  "mcpServers": {
    "rdf4j": {
      "command": "rdf4j-mcp",
      "args": ["--server-url", "http://localhost:8080/rdf4j-server", "--repository", "my-repo"]
    }
  }
}

Remote mode (HTTP):

{
  "mcpServers": {
    "rdf4j": {
      "url": "http://your-server:3000/sse"
    }
  }
}

VS Code

Create .vscode/mcp.json in your workspace:

Local mode:

{
  "servers": {
    "rdf4j": {
      "command": "rdf4j-mcp",
      "args": ["--server-url", "http://localhost:8080/rdf4j-server", "--repository", "my-repo"]
    }
  }
}

Remote mode:

{
  "servers": {
    "rdf4j": {
      "url": "http://your-server:3000/sse"
    }
  }
}

For GitHub Copilot, use @mcp in chat:

@mcp What classes are in the knowledge graph?

Features

MCP Tools

Tool	Description
`sparql_select`	Execute SELECT queries, returns JSON
`sparql_construct`	Execute CONSTRUCT/DESCRIBE, returns Turtle
`sparql_ask`	Execute ASK queries, returns boolean
`describe_resource`	Get all triples about an IRI
`search_classes`	Find classes by name pattern
`search_properties`	Find properties by pattern/domain/range
`find_instances`	Find instances of a class
`get_schema_summary`	Ontology overview with statistics
`list_repositories`	List available repositories
`get_namespaces`	Get namespace prefix mappings
`get_statistics`	Statement/class/property counts
`select_repository`	Switch active repository

MCP Resources

URI	Description
`rdf4j://repositories`	List of repositories
`rdf4j://repository/{id}/schema`	Schema summary
`rdf4j://repository/{id}/namespaces`	Namespace prefixes
`rdf4j://repository/{id}/statistics`	Repository statistics

MCP Prompts

Prompt	Description
`explore_knowledge_graph`	Guided exploration with schema context
`write_sparql_query`	Natural language to SPARQL
`explain_ontology`	Explain classes and relationships

Configuration

CLI Options

rdf4j-mcp (stdio mode):

--server-url URL    RDF4J server URL (default: http://localhost:8080/rdf4j-server)
--repository ID     Default repository ID
--debug             Enable debug logging

rdf4j-mcp-server (HTTP mode):

--host HOST         Bind address (default: 0.0.0.0)
--port PORT         Listen port (default: 3000)
--server-url URL    RDF4J server URL
--repository ID     Default repository ID
--reload            Auto-reload for development
--debug             Enable debug logging

Environment Variables

All use the RDF4J_MCP_ prefix:

Variable	Default	Description
`RDF4J_SERVER_URL`	`http://localhost:8080/rdf4j-server`	RDF4J server URL
`DEFAULT_REPOSITORY`	-	Default repository ID
`QUERY_TIMEOUT`	`30`	Query timeout (seconds)
`DEFAULT_LIMIT`	`100`	Default query LIMIT
`MAX_LIMIT`	`10000`	Maximum query LIMIT

Running RDF4J Server

Using Docker:

docker run -d -p 8080:8080 eclipse/rdf4j-workbench

Then create a repository at http://localhost:8080/rdf4j-workbench.

Or use the demo setup script which handles everything:

./examples/setup-demo.sh

Development

uv sync --dev

# Run tests
pytest

# Lint and format
ruff check src tests
ruff format src tests

# Type check
ty check src

License

MIT

RDF4J MCP Server

RDF4J MCP Server

Quick Demo

Installation

Two Ways to Run

Option 1: Local Mode (stdio)

Option 2: Remote Mode (HTTP/SSE)

Client Configuration

Claude Desktop

VS Code

Features

MCP Tools

MCP Resources

MCP Prompts

Configuration

CLI Options

Environment Variables

Running RDF4J Server

Development

License

Resources

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