New MCP service to use smart Data models in your local AI agent. Autonomous light management 4 Smart Data Models

Are you building AI applications for Smart Cities, Energy, or IoT? Interoperability is often the biggest hurdle—but a new tool is making it easier to keep your LLMs “in the loop” with global standards.

Introducing the Smart Data Models MCP Server, a new reference implementation that connects Large Language Models directly to the Smart Data Models ecosystem. Built on the Model Context Protocol (MCP), this server allows AI agents to browse, retrieve, and implement standardized data schemas in real-time.

Key Features:

  • Instant Schema Access: Give your LLM the ability to look up official data models for everything from street lighting to soil sensors.

  • Enhanced Accuracy: Reduce hallucinations by providing the AI with the exact JSON-LD and NGSI-LD structures required for your project.

  • Seamless Integration: Designed for easy setup with MCP-compatible clients (like Claude Desktop), enabling a smoother developer workflow for digital twin and IoT projects.

By providing LLMs with a “dictionary” of standardized data, the Smart Data Models MCP Server ensures that your AI-driven solutions are born interoperable.

Check it out on GitHub: agaldemas/smartdatamodels-mcp

In these slides are the detailed explanation of this  that can be reached here. See below the architecture and in the README the easy configuration.

Here there is the configuration file (there are several options)

{
  "mcpServers": {
    "smart-data-models-http": {
      "autoApprove": [],
      "disabled": false,
      "type": "streamableHttp",
      "timeout": 180,
      "url": "http://127.0.0.1:3210/mcp"
    }
  }
}

The source code is available here

Thanks to Alain Galdemas for the contribution

 

New pysmartdatamodels Version 0.8.0.9. Updated to 1066 data models

We are thrilled to announce a significant new release of our Python package, pysmartdatamodels, designed to empower developers and streamline the contribution process for our community.

This update is packed with new data models till 6-3-26.

Yo do not need to update the package if you use the function it will update the information about the new datamodels published:

from pysmartdatamodels import pysmartdatamodels as sdm
sdm.update_data()

It will take several minutes (depending on your connection because it updates 140 Mb)
 

BESSER Smart Data JSON Schema Generator

Overview

The tool automates the generation of Smart Data Models (SDM) from visual models, bridging the gap between high-level domain design and technical implementation for Digital Twins and IoT ecosystems.

Technical Workflow

  1. Input: Users define domain entities and relationships using B-UML (a simplified UML dialect) within the BESSER Pearl editor.

  2. Transformation: The engine maps these models to the NGSI-LD standard and Schema.org vocabularies.

  3. Output: For every entity, it automatically generates a compliant folder containing:

    • schema.json: The technical JSON Schema definition.

    • Examples: Multi-format payloads including JSON-LD, NGSI-v2, and Normalized NGSI-LD.

    • Documentation: Automatically derived human-readable specifications.

Key Advantages

  • Interoperability: Ensures 100% compliance with ETSI NGSI-LD and SDM contribution guidelines.

  • Model-Driven Engineering (MDE): Moves the “source of truth” to a visual model, reducing manual coding errors in complex JSON-LD structures.

  • Efficiency: Accelerates the deployment of standardized data spaces by automating the boilerplate required for context brokers (e.g., Orion).