Author: maestro

New data models mapping OpenStreetMap

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Fifteen new data models from the mapping of OpenStreetMap are now available in a new subject datamodel.OpenStreetMap

Thanks to the inspiration of Flavio Cirillo who currently works for NEC these models are now available, and thanks to the contributors of OpenStreetMap who created these models that are mapped here in this derivative work.

New data models will come shortly for this new subject

  • OSMAerialway. An aerial transport or lift from OpenStreetMap tagged with aerialway=*. Describes cable cars, gondolas, chair lifts, and drag lifts

  • OSMAmenity. A facility or amenity from OpenStreetMap tagged with amenity=*. Represents places of public or private use such as restaurants, schools, hospitals, banks, etc

  • OSMBuilding. A building or group of connected buildings from OpenStreetMap tagged with building=*. Represents the physical structure of a building, including its geometry, dimensions, materials, and functional classification. For specific functions within a building (shop, amenity, etc.) additional entities should be used.

  • OSMBuildingPart. A part or section of a building from OpenStreetMap tagged with building:part=*. Used to represent buildings with varying heights, roof shapes, or materials across different sections, enabling 3D building visualisation (Simple 3D Buildings standard).

  • OSMCycleRoute. A dedicated cycling infrastructure element from OpenStreetMap. Represents cycle paths, shared cycle lanes, and bike routes tagged with highway=cycleway or cycleway=* on road ways.

  • OSMHighway. A road or path element from OpenStreetMap tagged with the highway key. Represents all types of roads, from motorways to footpaths, as defined by the OSM highway taxonomy.

  • OSMLanduse. An area of land use from OpenStreetMap tagged with landuse=*. Describes the primary human purpose or use of an area of land

  • OSMLeisure. A place for leisure, sports, or recreation from OpenStreetMap tagged with leisure=*. Includes parks, sports pitches, swimming pools, stadiums, etc

  • OSMNatural. A natural feature from OpenStreetMap tagged with natural=*. Describes geographical features such as water bodies, woods, scrub, peaks, beaches, etc

  • OSMParkingArea. A vehicle parking area or facility from OpenStreetMap tagged with amenity=parking. Represents surface car parks, multi-storey garages, underground facilities, and other organised parking areas.

  • OSMPublicTransportStop. A public transport stop, station, platform or boarding point from OpenStreetMap. Represents nodes and areas where passengers board or alight from public transport services, tagged with public_transport=* or specific mode tags.

  • OSMRailway. A railway track or infrastructure element from OpenStreetMap tagged with the railway key. Covers all types of rail infrastructure, from mainline heavy rail to trams, subways and funiculars.

  • OSMShop. A shop or retail store from OpenStreetMap tagged with shop=*. Represents establishments selling retail products directly to end customers

  • OSMTourism. A tourist attraction or tourism-related facility from OpenStreetMap tagged with tourism=*. Includes hotels, museums, viewpoints, artworks, etc

  • OSMWaterway. A waterway from OpenStreetMap tagged with waterway=*. Describes rivers, streams, canals, drains, and other water courses

 

Use smartdatamodels in your local AI agent with this MCP server

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With this MCP configuration file you will be capable to use Smart Data Models locally in your AI agent.

{
  "mcpServers": {
    "smartdatamodels": {
      "type": "http",
      "serverUrl": "https://opendatamodels.org/mcp/v1"
    }
  }
}
More information here
These functions are available
1. search_data_models
Search the Smart Data Models (SDM) catalog by keyword, domain name, or partial model name. Use this when the user wants to discover which data models exist for a topic (e.g., ‘parking’, ‘weather’, ‘energy meter’). Returns a list of matching model names and their subjects. Example: search_data_models({“query”: “air quality”, “domain”: “SmartEnvironment”})
2. get_data_model
Retrieve the complete JSON Schema and metadata for a specific Smart Data Model. Use this when the user already knows the model name and wants its full definition, required fields, attribute types, or documentation URL. Example: get_data_model({“model_name”: “WeatherObserved”, “include_examples”: true})
3. list_domains
List all available SDM domains (top-level industry categories) with the count of data models in each. Use this as the entry point when the user wants an overview of what sectors are covered, or before calling list_models_by_domain. No parameters required. Example: list_domains({})
4. list_models_by_domain
List all data model names available within a specific SDM domain. Use this when the user wants to browse all models in a sector such as SmartCities or SmartHealth. Call list_domains first to confirm valid domain names. Example: list_models_by_domain({“domain”: “SmartEnergy”})
5. validate_data
Validate a JSON payload against the schema of a specific Smart Data Model. Use this when the user has an IoT payload or NGSI-LD entity and wants to check if it conforms to the standard. Returns a structured result with pass/fail and a list of specific validation errors if any. Example: validate_data({“model_name”: “WeatherObserved”, “data”: {“id”: “urn:ngsi-ld:WeatherObserved:001”, “type”: “WeatherObserved”, “temperature”: {“type”: “Property”, “value”: 22.3}}})
6. get_related_models
Find Smart Data Models that are semantically related to a given model. Useful for discovering adjacent models when building multi-entity systems — e.g., related models for ‘OffStreetParking’ might surface ‘ParkingSpot’ and ‘ParkingGroup’. Example: get_related_models({“model_name”: “OffStreetParking”, “relationship_type”: “all”})
7. get_attributes_for_model
List all attributes (properties) of a specific Smart Data Model, including each attribute’s NGSI type (Property, GeoProperty, or Relationship), data type, description, recommended units, and reference model URL. Use this after get_data_model when the user wants to understand what fields a model has, what values they accept, or how to construct a valid NGSI-LD payload. Example: get_attributes_for_model({“model_name”: “WeatherObserved”})
8. get_attribute_details
Get full details for a single attribute within a Smart Data Model — including its description, NGSI type (Property/GeoProperty/Relationship), data type, recommended units, format constraints, and reference model URL. Use this when the user asks what type a specific field is, what units it uses, or how to populate it. Example: get_attribute_details({“model_name”: “WeatherObserved”, “attribute”: “temperature”})
9. find_subject_by_model_name
Find which SDM subject (repository group, always prefixed with ‘dataModel.’) a data model belongs to, given only the model name. Use this when you have an entity type from an incoming NGSI-LD payload but don’t know its subject — for example, if a payload contains ‘type: WeatherObserved’ and you need to resolve its subject before calling get_data_model or validate_data. Example: find_subject_by_model_name({“model_name”: “WeatherObserved”})
10. fuzzy_find_model
Search for a data model by approximate or misspelled name using fuzzy matching. Use this as the recovery step whenever get_data_model returns MODEL_NOT_FOUND — it finds the closest real model names even when the spelling is off. Returns ranked candidates with similarity scores. Example: fuzzy_find_model({“model_name”: “WeatherFora”, “threshold”: 80})
11. get_subject_repo_url
Return the GitHub repository URL for an SDM subject (e.g., ‘dataModel.Weather’). Use this when the user wants to browse the source files, open issues, or cite the authoritative schema location for a subject group. Example: get_subject_repo_url({“subject”: “dataModel.Weather”})
12. get_model_repo_url
Return the GitHub repository URL(s) for a specific data model by name. Use this when the user needs a direct link to the schema source, examples, or documentation on GitHub. Example: get_model_repo_url({“model_name”: “WeatherObserved”})
13. get_full_catalog
Return the complete list of all data models within a specific domain, including names, subjects, descriptions, and repository links. Use this when the user needs a comprehensive structured overview of an entire domain for bulk processing, analysis, or selection. A domain filter is mandatory to prevent returning the entire catalog in one call. Example: get_full_catalog({“domain”: “SmartEnvironment”})
14. export_model_attributes
Export the attributes of a data model as a flat delimited string, with caller-selected meta-fields per attribute. Use this when a data engineer needs a CSV-ready or pipe-delimited listing for database column mapping, ontology alignment, or spreadsheet import. Available meta-fields: property, type, dataModel, repoName, description, typeNGSI, modelTags, format, units, model. Example: export_model_attributes({“model_name”: “WeatherObserved”, “separator”: “,”, “fields”: [“property”, “type”, “typeNGSI”, “units”]})
15. get_model_context
Return the NGSI-LD @context URL and context content for a specific data model. This is required when building valid NGSI-LD linked-data payloads — without the correct @context, the payload is not valid linked data and will be rejected by NGSI-LD brokers. Use this before constructing or validating a normalized payload. Example: get_model_context({“model_name”: “WeatherObserved”})

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

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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

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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)