RDF Explorer v1.0.0

RDF Explorer is a Model Context Protocol (MCP) server designed to bridge the gap between AI assistants and RDF‑based knowledge graphs. It offers a conversational interface that allows developers to interrogate, analyze, and visualize graph data either from local Turtle files or through external SPARQL endpoints. By exposing a rich set of tools, resources, and prompts, the server transforms complex graph operations into natural language‑driven interactions that can be seamlessly integrated into AI workflows.

The core problem RDF Explorer solves is the difficulty of accessing and manipulating graph data from within conversational agents. Traditional SPARQL clients require specialized knowledge of query syntax, while local file handling demands separate tooling. RDF Explorer abstracts these complexities behind simple MCP commands: a host can issue a natural‑language request such as “Show me all classes” or “Find relationships for ”, and the server will translate it into a SPARQL query, execute it against the configured data source, and return results in a structured format. This lowers the barrier for AI assistants to perform knowledge‑graph research, data preparation, and semantic reasoning tasks.

Key capabilities of the server include:

• Dual‑mode operation: Work in Local File mode for quick prototyping with Turtle datasets or switch to SPARQL Endpoint mode for production‑grade, federated queries.
• Comprehensive toolset: Execute arbitrary SPARQL queries (, ), perform full‑text searches, gather graph statistics, and check triplestore health.
• Schema discovery: Retrieve classes and properties via , enabling AI agents to understand the underlying ontology.
• Predefined queries and templates: Access ready‑made exploratory queries () or SPARQL templates (), reducing the need to craft queries from scratch.
• Prompt‑based query generation: Convert natural language prompts into SPARQL () and guide users through graph analysis with and .

Typical use cases span a wide range of scenarios. In research, scientists can query publication metadata or citation networks without leaving their conversational interface. Data engineers may audit graph integrity, count triples, and generate markdown reports () for documentation. Knowledge‑graph developers can prototype federated queries against public endpoints and iterate quickly, while data scientists can pull schema insights to inform feature engineering for downstream AI models.

Integration into existing AI workflows is straightforward: an MCP‑enabled host sends a tool invocation or resource request, receives JSON or Markdown responses, and feeds them back into the assistant’s context. This tight coupling allows for dynamic question answering, automated data exploration, and real‑time graph analytics—all within a single conversational session. RDF Explorer’s ability to toggle between local and remote datasets, coupled with its extensive tool coverage, makes it a standout choice for developers seeking to embed semantic graph intelligence into AI applications.