Following the release of Anthropic's newest Claude model, Fable 5, HighQSoft ran a focused experiment. Could an AI agent derive a reusable skill that creates ASAM ODS dashboards on measured data?

The specification was about 20 lines long and contained mostly boundary conditions, namely Streamlit as the dashboard framework, pyHQL for data access, and the HighQSoft MCP Server for ASAM ODS as the discovery layer. The dashboards themselves were a free pass.

The directions were to discover the database, create five dashboards, keep the skill generic by representation type (graph, geo, table), offer features such as sliders and filters based on the discovered application model, and reinforce lessons learned from each step.

The agent ran for 25 minutes with one manual feedback loop. The result on HighQSoft's weather demonstration database was five working dashboards, a Channel Explorer, a Climate Map, Database Statistics, Last Data Imports, and a Measurement Channel Comparison. Every dashboard runs on live HQL queries against the ODS server. No data was exported, copied, or staged.

Two components made the result possible.

• First, the HQL engine accessed through pyHQL. The agent had the complete query language context available and validated every query against the live server before baking it into an app. What ships is what ran.
• Second, version 2 of the MCP Server for ASAM ODS, rebuilt on FastMCP 3. The key design change in version 2 was the addition of two tools, discover_model and find_paths. Together they let the agent resolve the customer-specific application model instead of guessing element and attribute names. The MCP Server simply knows what the database looks like.

The five dashboards are the visible output, but the actual result is the skill that now builds the next ones. Because the skill works from model discovery rather than hard-coded names, it transfers to any ASAM ODS application model. Test engineers describe the view they want in plain language, and the skill composes, validates, and ships a live dashboard from there.

#ASAM #ODS #AI #MCP #HQL #Streamlit #TestData #HighQSoft

The article then shows how HighQSoft's stack already plugs AI into the standard. The MCP Server connects Claude and ChatGPT to ASAM ODS through HQL. Merlin AI extends the same pattern to analysis pipeline composition. MoMa AI, from the AEOPT research project, generates importer mappings from sample data so a new measurement source becomes queryable in hours.

*#ASAM #ODS #AI #TestData #HQL #MCP #HighQSoft*