Back to AI News
arxiv

tok: A Textual Concrete Syntax for BPMN/DMN with LLM and MCP Support

The paper presents tok, a plain-text notation for BPMN 2.0.2 process models and DMN 1.5 decisions, with an engine that checks documents against both standards, compiles them to XML with automatic diagrams, and serves language-model applications through the Model Context Protocol. It reports that tok models use fewer tokens than BPMN XML, that its checker detects 98.2% of injected faults versus 85.8% for XML Schema with bpmnlint, and that new language models write valid tok from a one-page reference. Agent evaluations with a lean skill and checker tool reached 97.6% to 100% valid tok across 87 process descriptions, and 44 of 47 simulated event logs fit their source models.

AI Brief

Useful for teams building AI agents around business-process modeling because it offers a compact, checkable text syntax and MCP tooling with reported LLM usability results.

arXiv:2611.07945v1 Announce Type: cross Abstract: tok is a plain-text notation for Business Process Model and Notation (BPMN) 2.0.2 process models and Decision Model and Notation (DMN) 1.5 decisions. One engine checks a tok document against both standards, compiles the document to XML with an automatic diagram, and serves language-model applications through the Model Context Protocol. The paper classifies the notation as an alternative concrete syntax of the BPMN and DMN metamodels and reports one study. A tok model takes 2.2 times fewer tokens than the smallest BPMN XML of 893 files exported by 37 tools and 7.7 times fewer than the files as exported. The checker signals 98.2% of 1,411 injected faults, against 85.8% for the XML Schema with bpmnlint. Four language models new to tok write valid tok from a one-page reference for 85.7 to 100% of 87 process descriptions from two datasets and valid BPMN XML for every description, and the strongest model writes tok with 25% and 19% fewer tokens than BPMN XML. As agents with a lean skill and one checker tool, the models write valid tok for 97.6 to 100% of the descriptions, the strongest model spends 21% and 14% fewer tokens on tok than on BPMN XML, and a skill which sends the model to read the reference through a tool costs 1.7 to 2.8 times the tokens of the lean skill. Of 47 event logs simulated from public models, 44 fit their source models, and tok imports every model discovered from the logs.
Be the first to rate this article.

Comments & Questions (0)

Captcha
Please be respectful — let's keep the conversation friendly.

No comments yet

Be the first to comment!