flowchart LR
  A[("ontology/simonto.ttl<br/>OWL schema")] --> G
  B[("data/aeroflow.ttl<br/>product surface")] --> G
  G{"RDF graph<br/>(rdflib)"} --> V["SHACL + graph checks<br/>ontology/shapes.ttl"]
  G --> P["SPARQL property paths<br/>plan / impact / lineage"]
  G --> T["Tool generator<br/>JSON Schema + annotations"]
  G --> R["Agentic-readiness<br/>review (100-pt rubric)"]
  T --> M["MCP server (stdio)<br/>18 tools"]
  P --> M
  M --> X["Simulated backend<br/>arg + prerequisite enforcement"]
  V -. CI gate .-> CI(["GitHub Actions"])

pip install -e ".[dev]"      # rdflib + pyshacl (+ pytest)
python -m simonto validate   # SHACL + graph checks
python -m simonto demo       # scripted agent: fails, asks the ontology, recovers, finishes
python -m pytest -q          # 34 tests, incl. an end-to-end MCP subprocess test

{ "mcpServers": { "simonto-aeroflow": { "command": "simonto", "args": ["serve"] } } }

> predict_with_surrogate {"inlet_velocity": 60, "angle_of_attack": 4}
  ERROR missing_prerequisite: predict_with_surrogate needs SurrogateModel, which does not exist in this session yet
  hint: call ontology_plan_workflow with goal='predict_with_surrogate' to get the ordered steps

> generate_mesh {"global_size": 0.01}
  ERROR invalid_arguments: 'global_size'=0.01 is below the minimum 0.1 mm

flowchart LR
  subgraph PreprocessingOperation["PreprocessingOperation"]
    check_mesh_quality[check_mesh_quality]
    configure_solver[configure_solver]
    generate_mesh[[generate_mesh]]
    import_geometry[import_geometry]
    repair_geometry[repair_geometry]
    set_boundary_conditions[set_boundary_conditions]
  end
  subgraph PostprocessingOperation["PostprocessingOperation"]
    export_results[export_results]
    extract_forces[extract_forces]
  end
  subgraph SolveOperation["SolveOperation"]
    monitor_convergence[monitor_convergence]
    run_solver[[run_solver]]
  end
  subgraph SurrogateOperation["SurrogateOperation"]
    predict_with_surrogate[predict_with_surrogate]
    record_design_point[record_design_point]
    train_surrogate[[train_surrogate]]
  end
  generate_mesh --> check_mesh_quality
  check_mesh_quality --> configure_solver
  set_boundary_conditions --> configure_solver
  run_solver --> export_results
  run_solver --> extract_forces
  repair_geometry --> generate_mesh
  run_solver --> monitor_convergence
  train_surrogate --> predict_with_surrogate
  extract_forces --> record_design_point
  import_geometry --> repair_geometry
  configure_solver --> run_solver
  generate_mesh --> set_boundary_conditions
  record_design_point --> train_surrogate

$ python -m simonto --data data/legacy_sample.ttl validate    # fails: 11 SHACL errors + 1 dangling input
$ python -m simonto --data data/legacy_sample.ttl readiness
 25  run_it    description is 8 chars; thin parameter descriptions; missing unit: n; missing min/max: n; enum without allowed values; no output artifact declared
 85  report    consumes RunOutput without a declared prerequisite that produces it
overall 55.0 / 100

python -m simonto query -f queries/prerequisites_of.rq      # transitive prerequisites of run_solver
python -m simonto query -f queries/unsafe_operations.rq     # destructive or long-running: confirm first
python -m simonto query -f queries/artifact_lineage.rq      # who produces / consumes each artifact
python -m simonto query -f queries/parameters_with_units.rq # the numeric contract: unit + safe range

ontology/   simonto.ttl (OWL schema), shapes.ttl (SHACL)
data/       aeroflow.ttl (fictional product), legacy_sample.ttl (poor example)
queries/    SPARQL examples
src/simonto graph, validate, planner, toolgen, readiness, backend, mcp_server, cli
tests/      34 tests (ontology, planner, tool generation, backend, readiness, MCP e2e)
docs/       generated: tools.json, workflow.mmd, readiness_report.md