Yep! AI Agents need a steady, reliable, and secure flow of data that transcends infrastructure hurdles. Take a look at how we (OpenLink Software) offer sophisticated data access and connectivity plumbing for Linux by combing existing open standards such as ODBC and JDBC.

It’s been a heavily ODBC- and JDBC-focused period for the OpenLink Software team. Why? Because Open Data Connectivity is the foundation of the vision expressed in our vertically integrated product portfolio—an area seeing increased demand in the age of AI. For instance, we’ve long

MCP is sometimes referred to as “ODBC for LLMs” or even “CORBA for LLMs.” Either way, OpenLink Software is well-versed in both, so we’ve been working on something that provides yet another entry point into our world of sophisticated and secure data access, driven by existing open

Anthropic OpenLink Software Cline Visual Studio Code Here's a variation of the prior screencast demo, but showcasing Anthropic Claude Desktop edition using our MCP Server for ODBC. The first demo is a basic SQL query, while the second is a SPARQL query implemented as a subquery. This is the kind of data access and connectivity

Anthropic OpenLink Software Cline Visual Studio Code Here's a link to the Anthropic Claude session used to produce the demo. claude.ai/share/8c43819f… You will be able to replicate this demo once we release our little piece of Typescript for creating an MCP Server for ODBC using node.js.

Another day, another "Semantic Web Project" easy button demo, this time using Cursor and our new mcp-server for ODBC. Semantic Web Project connection? This is in relation to the SPASQL (SPARQL inside SQL) example using the DBpedia Knowledge Graph as the target data source.

In the age of AI, these connectors (drivers) for Linux will be crucial to Retrieval Augmented Generation (RAG) workflows handled via LLM extensions using External Function Calling (Tools) or the Model Context Protocol (MCP). Whatever route works for you, OpenLink Software's got you 😃

Boom! This release comprises critical server-side foundation for truly harnessing the potential of recent AI innovations at both personal and enterprise levels covering: 1. Powerful harmonization of data across disparate ODBC-, JDBC-, or HTTP-accessible line-of-business

Here's another "easy button" example showcasing the fundamental vision of the Semantic Web Project, powered by our Virtuoso platform. I'm demonstrating our upcoming SQLAlchemy MCP server, built for Python developers using the SQLAlchemy Object-Relational Layer and the PyODBC

Today's 'Easy Button' demo for the 'Semantic Web Project Utility'' combines MCP, an LLM, ODBC, SQL, SPASQL, and SPARQL. In a nutshell, it showcases loose coupling of existing open standards to create data flow that isn't impeded by query language, solution formats, etc. I

Q: Amongst all of Virtuoso's data access, integration, and management magic, is it capable of enabling interaction with other AI Agents via the soon to be released MCP server for ODBC? A: Of course! The OpenLink Software AI Layer (OPAL) is a sophisticated add-on to Virtuoso, created

MCP implementations are built using SDKs that currently exist for both TypeScript and Python. We simply leveraged the existing node-odbc layer for the TypeScript implementation of the mcp server, and SQLAlchemy + PyODBC for the Python implementation. Both of these mcp servers

In the age of MCP, courtesy of our ODBC to JDBC Bridge Drivers, this also means transparently integrating Neo4j managed graphs into RAG (or GraphRAG) workflows orchestrated by LLM-based MCP clients.

Boom! Anthropic Claude Desktop's UI update now makes it even easier to view *tools* associated with installed MCP servers. Invocation pattern takes the following forms: @{mcp-server-name}.{tool-name}.

Here's a link to the newsletter post. linkedin.com/pulse/whats-mo… #MCP #ODBC #DataConnectivity #AI #GenAI #Explainer #HowTo

Q: Given an RDF-based data dictionary (or Ontology) for Northwind, can an LLM generate accurate SPARQL queries? A: Yes, depending on the LLM you choose. Ontology ID: demo.openlinksw.com/schemas/Northw… See comments section for results across OpenAI's GPT-4.1, @Grok 3 beta, and

OpenAI GPT-4.1 Results page, courtesy of the OpenLink Software AI Layer functioning as an API client: linkeddata.uriburner.com/chat/?chat_id=… -- Static linkeddata.uriburner.com/chat/?chat_id=… -- Animated

OpenAI OpenLink Software Grok 3 beta results page. linkeddata.uriburner.com/chat/?chat_id=… -- Static linkeddata.uriburner.com/chat/?chat_id=… -- Animated

OpenAI OpenLink Software Grok Google Gemini 2.5 experimental results page. linkeddata.uriburner.com/chat/?chat_id=… -- Static linkeddata.uriburner.com/chat/?chat_id=… -- Animated

OpenAI OpenLink Software Grok Google @DeepSeek, Qwen, Anthropic Sonnet 3.7, Mistral AI, Meta Llama (prior and latest) currently fail this test. What's the test again? Given an Ontology, write a SPARQL query that retrieves information about product sales performance by category from a designated data