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MCP Server for Apache Jena: Scalable SPARQL & Data Integration - MCP Implementation

MCP Server for Apache Jena: Scalable SPARQL & Data Integration

Empower Apache Jena projects with MCP Server’s scalable SPARQL endpoints and seamless data integration, boosting semantic web app performance for enterprise-ready deployments.

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About MCP Server for Apache Jena

What is MCP Server for Apache Jena: Scalable SPARQL & Data Integration?

MCP Server bridges AI agents and the Apache Jena RDF platform, enabling seamless SPARQL query execution and data manipulation. This server acts as an intermediary, allowing tools like Cursor or Claude agents to interact with Jena Fuseki's RDF datasets through the Model Context Protocol (MCP). It supports both read and write operations, including graph management and authentication, ensuring secure and efficient integration with semantic web data.

How to Use MCP Server for Apache Jena: Scalable SPARQL & Data Integration?

Installation requires Node.js v16+, a running Jena Fuseki instance, and an MCP-compatible AI agent. Follow these steps:

  1. Clone the repository and install dependencies via npm.
  2. Build the TypeScript code using npm run build.
  3. Launch the server with default settings or customize parameters using CLI flags/environment variables for endpoint URLs, datasets, and credentials.

Development mode enables live code reloading for iterative testing. Refer to environment variables like JENA_FUSEKI_URL or API_KEY for advanced configuration.

MCP Server for Apache Jena Features

Key Features of MCP Server for Apache Jena: Scalable SPARQL & Data Integration?

This server provides three core tools:

  • SPARQL Query Execution: Run SELECT/ASK queries against named graphs in Jena datasets.
  • Data Manipulation: Use INSERT/DELETE updates to modify RDF triples in real time.
  • Graph Management: List available named graphs to discover dataset structure programmatically.

Additional capabilities include HTTP Basic authentication support and compatibility with MCP-enabled AI systems for enterprise-grade data workflows.

Use Cases of MCP Server for Apache Jena: Scalable SPARQL & Data Integration?

Common applications include:

  • Enabling AI agents to analyze knowledge graphs for decision-making systems
  • Automating SPARQL-based data synchronization between heterogeneous systems
  • Building interactive SPARQL playgrounds for data scientists
  • Implementing audit trails through controlled update operations

MCP Server for Apache Jena FAQ

FAQ from MCP Server for Apache Jena: Scalable SPARQL & Data Integration?

How does authentication work?

Supports HTTP Basic authentication via CLI flags or environment variables. For added security, combine with API keys configured through API_KEY.

Can I use this with cloud-based Jena instances?

Yes, by specifying the Jena endpoint URL via --endpoint. Works with both local and remote Fuseki deployments.

What SPARQL versions are supported?

Compatible with SPARQL 1.1 as implemented by Jena, including all standard query/update operations.

How scalable is the solution?

Performance scales with underlying Jena configuration. Horizontal scaling can be achieved by deploying multiple server instances behind a load balancer.

Content

MCP Server for Apache Jena

A Model Context Protocol (MCP) server that connects AI agents to Apache Jena for SPARQL query capabilities.

Overview

This project implements an MCP server that allows AI agents (such as Cursor, Claude for Cline, or Claude Desktop) to access and query RDF data stored in Apache Jena. The server provides tools for executing SPARQL queries and updates against a Jena Fuseki server.

Features

  • Execute SPARQL queries against a Jena Fuseki server
  • Execute SPARQL updates to modify RDF data
  • List available named graphs in the dataset
  • HTTP Basic authentication support for Jena Fuseki
  • Compatible with the Model Context Protocol

Prerequisites

  • Node.js (v16 or later)
  • Apache Jena Fuseki server running with your RDF data loaded
  • An AI agent that supports the Model Context Protocol (e.g., Cursor, Claude for Cline)

Installation

  1. Clone this repository:

    git clone https://github.com/ramuzes/mcp-jena.git

cd mcp-jena
  1. Install dependencies:

    npm install

  2. Build the TypeScript code:

    npm run build

Usage

Run the server with default settings (localhost:3030 for Jena, 'ds' for dataset):

npm start

Or specify custom Jena endpoint, dataset, and authentication credentials:

npm start -- --endpoint http://your-jena-server:3030 --dataset your_dataset --username your_username --password your_password

You can also use short flags:

npm start -- -e http://your-jena-server:3030 -d your_dataset -u your_username -p your_password

For development mode with automatic transpilation:

npm run dev:transpile -- -e http://your-jena-server:3030 -d your_dataset -u your_username -p your_password

Available Tools

This MCP server provides the following tools:

  1. execute_sparql_query - Execute a SPARQL query against the Jena dataset
  2. execute_sparql_update - Execute a SPARQL update query to modify the dataset
  3. list_graphs - List all available named graphs in the dataset

Environment Variables

You can also configure the server using environment variables:

  • JENA_FUSEKI_URL: URL of your Jena Fuseki server (default: http://localhost:3030)
  • DEFAULT_DATASET: Default dataset name (default: ds)
  • JENA_USERNAME: Username for HTTP Basic authentication to Jena Fuseki
  • JENA_PASSWORD: Password for HTTP Basic authentication to Jena Fuseki
  • PORT: Port for the MCP server (for HTTP transport, default: 8080)
  • API_KEY: API key for MCP server authentication

Example SPARQL Queries

Basic SELECT query:

SELECT ?subject ?predicate ?object
WHERE {
  ?subject ?predicate ?object
}
LIMIT 10

Insert data with UPDATE:

PREFIX ex: <http://example.org/>
INSERT DATA {
  ex:subject1 ex:predicate1 "object1" .
  ex:subject2 ex:predicate2 42 .
}

Query a specific named graph:

SELECT ?subject ?predicate ?object
FROM NAMED <http://example.org/graph1>
WHERE {
  GRAPH <http://example.org/graph1> {
    ?subject ?predicate ?object
  }
}
LIMIT 10

Resources

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