Kratos MCP Server

An MCP server that lets AI assistants drive Kratos Multiphysics finite element simulations end to end:

• Introspect the installation: applications, elements, conditions, constitutive laws, variables, solvers and processes and their default parameters (process defaults are parsed from the Kratos source, no build needed).
• Scaffold simulation cases from templates: structural (static/dynamic/modal), thermal (transient/stationary), fluid (monolithic or fractional-step incompressible) and potential flow — plus multi-stage orchestrated cases that chain analyses. Curated material and linear-solver presets included. ProjectParameters.json, Materials.json and structured MDPA meshes with named boundary regions.
• Study parameter sweeps and structured mesh convergence with isolated variants, a bounded background queue, and physical-location probe responses (tools, tutorial).
• Run simulations (single- or multi-stage) as managed background jobs (status, live logs, progress, cancel) that survive server restarts, with optional immutable input snapshots, reproducible reruns, and checkpoint resume for isolated serial single-stage jobs.
• Post-process VTK results: summaries, point probes, convergence analysis, physical-time histories, CSV export, and reference comparisons.
• Preview results without ParaView: PNG screenshots and GIF animations (deformed shapes, field contours) rendered with pyvista and shown inline in the conversation — optional viz extra.
• Interoperate: explain an existing ProjectParameters.json, and convert cases to/from the Kratos FlowGraph visual node editor (lossless round-trip).

52 tools, 15 resources (10 worked examples) and 5 guided prompts. See the full documentation in docs/ (VitePress).

Quick start

A local Kratos build is not required — the server can pip-install Kratos itself on first use.

Once published to PyPI, no clone needed — uvx fetches and runs it:

claude mcp add kratos -- uvx kratos-mcp-server

From a local checkout (current state, before the first PyPI release):

git clone https://github.com/loumalouomega/Kratos-MCP-Server
cd Kratos-MCP-Server
uv sync
claude mcp add kratos -- uv --directory "$PWD" run kratos-mcp

Then ask your assistant something like:

Check the Kratos installation — install it if it's missing — then set up a cantilever plate 1 m × 0.2 m fixed on the left with a 1 MN/m downward load on the right edge, run it, and report the tip deflection.

The assistant calls kratos_install the first time and reuses it afterwards. If you already have a compiled Kratos checkout, skip that and point KRATOS_ROOT at it instead (-e KRATOS_ROOT=/path/to/Kratos on the claude mcp add line) — see Installation.

Notebooks

Five notebooks drive the server interactively as an MCP client — no AI assistant involved — each touching most of the relevant tools, resources and prompts in one sitting. Run any with uv sync --extra viz --group dev (adds ipykernel + pyvista) and open it in Jupyter/VS Code against that .venv.

• notebooks/cantilever.ipynb: the structural cantilever case — installation introspection, mesh generation, scaffolding, a background job you poll while it runs, VTK post-processing, a rendered PNG and an animated GIF, and cancelling a job in flight.
• notebooks/naca_airfoil.ipynb: a NACA0012 airfoil in incompressible laminar flow — reuses a real ~21k-node airfoil mesh from Kratos's own examples repo (simplified physics; see the notebook/tutorial for what and why), computes lift/drag by summing REACTION over the airfoil surface, and renders/animates the pressure field cropped to the airfoil with the newer crop_bounds option.
• notebooks/fluid_cavity.ipynb: the lid-driven cavity CFD benchmark — introspect the fluid solver, run the shipped example, probe the velocity field, and render an inline PNG and GIF of the recirculating vortex.
• notebooks/materials.ipynb: the materials surface — material & linear-solver presets, process-defaults introspection, and a single-element von Mises plasticity run showing the elastic→plastic transition.
• notebooks/multistage.ipynb: multi-stage orchestration — create_multistage_project chaining two load steps, explain_project_parameters, and a lossless round-trip to/from a Kratos FlowGraph node graph.
• docs/tutorials/potential-flow.md: a runnable NACA0012 perturbation potential-flow benchmark with upstream reference values.

Requirements

• Python ≥ 3.10, uv
• Kratos Multiphysics, either pip-installed via kratos_install (Linux/Windows x86_64 only — no macOS wheels) or a compiled build (tested with 10.4, StructuralMechanics / ConvectionDiffusion / FluidDynamics / LinearSolvers applications)
• Optional, for results_render/results_animate: the viz extra (uv sync --extra viz or pip install 'kratos-mcp-server[viz]') and a working OpenGL context (on headless machines: Xvfb or OSMesa VTK wheels — see docs/tools/visualization.md)

Architecture in one paragraph

Kratos is never imported in the server process — it prints a banner on import (which would corrupt the stdio JSON-RPC stream) and can abort the process on solver errors. Short operations run in a worker subprocess that returns JSON through a result file; simulations run detached with per-job directories under ~/.kratos-mcp/jobs/. See docs/guide/architecture.md.

Development

See the roadmap for planned work and the changelog for release history.

uv run pytest -m "not kratos"   # unit tests (no Kratos needed)
uv run pytest -m kratos          # integration tests against the real build
npm install && npm run docs:dev  # documentation site

License

MIT