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Ottosim — Simulation MCP Server

de ottonicversión 0.3.0i★ –iSe ejecuta en tu computadora
Nombre en el registroio.github.ottonic/simulation-mcp-server

Instalar

Resumen

Descripción del creador (original en inglés)

Thermal, structural, modal, DC, and laminar-flow studies from STEP via Gmsh and Elmer.

Los tres pilares de PopNx MCP

Estos tres nunca se combinan en una sola puntuación: las opiniones, las mediciones y la popularidad son cosas distintas.

Qué puedes hacer con él

Todavía no escribimos un resumen sencillo para este. Abajo están los datos que leímos del registro.

  • Conexión: Se ejecuta en tu computadora
  • Transporte: stdio
  • Categoría: Otros

Instalación

Elige la app de IA que usas y copia la configuración.

Antes de instalar

Necesitas

  • npmNode.js instalado (para el comando npx)

¿Qué app usas?

Elige tu app arriba. La recordamos, así que la próxima vez el botón dirá “Instalar en …”.

Abre Claude Desktop → Settings → Developer → Edit Config y pega esto en claude_desktop_config.json

{
  "mcpServers": {
    "simulation": {
      "command": "npx",
      "args": [
        "-y",
        "simulation-mcp-server"
      ]
    }
  }
}

Ejecuta esto en tu terminal

claude mcp add simulation -- npx -y simulation-mcp-server

Abrir en Cursor

O copia la configuración tú

Pégalo en .cursor/mcp.json de tu proyecto, o en ~/.cursor/mcp.json

{
  "mcpServers": {
    "simulation": {
      "command": "npx",
      "args": [
        "-y",
        "simulation-mcp-server"
      ]
    }
  }
}

Tu navegador te pide permiso para abrir la app; luego la app muestra la configuración y te pide confirmar. Los valores de las claves quedan vacíos para que los completes tú.

Abrir en VS Code

O copia la configuración tú

Pégalo en .vscode/mcp.json de tu proyecto

{
  "servers": {
    "simulation": {
      "type": "stdio",
      "command": "npx",
      "args": [
        "-y",
        "simulation-mcp-server"
      ]
    }
  }
}

Tu navegador te pide permiso para abrir la app; luego la app muestra la configuración y te pide confirmar. Los valores de las claves quedan vacíos para que los completes tú.

Este se ejecuta en tu computadora, y ChatGPT solo puede conectarse a servidores en internet. Elige otra app.

Tu elección se guarda en una cookie en este dispositivo; no necesitas cuenta.

Esta configuración se genera a partir de la ficha del creador en el registro. Si el README dice otra cosa, sigue el README.

Qué puede hacer

Se ejecuta en tu computadora; la lista de herramientas aparece en tu app de IA después de instalarlo.

Revisiones básicasi

Revisamos información que cualquiera puede ver; no hemos leído el código. Las revisiones que no aplican no se cuentan. · Revisado el 6 nov 2026

  • ✓ Superada

    El creador coincide con el repo

    El nombre en el registro se verificó con la cuenta de GitHub ottonic, dueña del repo

  • … Aún sin revisar

    Sigue en mantenimiento

    Todavía no hemos leído este repo en GitHub

  • … Aún sin revisar

    Tiene licencia

    Todavía no hemos leído este repo en GitHub

  • – No aplica

    El endpoint responde

    Este se ejecuta en tu computadora; no hay endpoint en internet

  • – No aplica

    Inicio de sesión con OAuth

    Se ejecuta en tu computadora y usa ajustes locales en su lugar

  • … Aún sin revisar

    Sin instrucciones ocultas en las descripciones de herramientas

    Se ejecuta en tu computadora; solo podemos leer listas de herramientas de endpoints en internet

Comparado con otros en Otrosi

  • Más estrellas que el 0% de los otros 32455 servidores de esta categoría
  • Publicado en el registro más recientemente que el 84% de los otros 32455 servidores de esta categoría
  • Revisiones básicas iguales o mejores que el 100% de los otros 32455 servidores de esta categoría

Comparado con todos los servidores activos de esta categoría en el registro. Las estrellas solo cuentan cuando quien lo publica es dueño del repo.

Preguntas frecuentes

¿Es seguro Ottosim — Simulation MCP Server?

Superó 1 de 1 revisiones básicas aplicables. No hemos leído el código. Antes de agregar una clave o un token, revisa qué acceso pide el servidor y usa una clave que tenga solo los permisos que necesitas.

¿Qué claves o códigos necesita?

El registro no indica ninguna clave obligatoria, aunque algunos servidores te piden iniciar sesión la primera vez que los usas.

¿Se ejecuta en mi computadora o en la nube?

Se ejecuta en tu computadora. Tu app de IA lo inicia cuando hace falta, y los datos no pasan por los servidores del creador (a menos que el propio servidor llame a un servicio en línea).

¿Sigue en mantenimiento?

El repo de GitHub cambió por última vez –, y la última versión (0.3.0) llegó al registro hace 6 días.

¿Qué apps pueden usarlo?

Las apps compatibles con MCP, como Claude Desktop, Claude Code, Cursor y VS Code. También necesitas Node.js en tu computadora.

¿Qué puedo usar en su lugar?

En la misma categoría: Spec Driven Development, Remote Desktop Commander, SSTracker free youth training

Alternativas

Servidores de la misma categoría hechos por otros creadores, ordenados por estrellas.

Cómo leer una tarjeta:★ Estrellas de GitHubiTres pilares: reseñas · revisiones · usoiActualizado = fecha en que se publicó la última versión en el registroi

Spec Driven Development

de formulahendry

MCP Server that facilitates spec-driven development workflows, not just Vibe Coding.

Se ejecuta en tu computadora★ 440MITActualizado hace 14 meses
Instalar

Remote Desktop Commander

de desktop-commander

Remote Desktop Commander: use files, terminal and processes on your own computers from any AI app.

Se ejecuta en la nube★ 211Actualizado ayer
Instalar

README del creador

Abrir el README

Copiado tal cual del repo del creador. PopNx MCP no lo ha revisado ni lo respalda. Se quitaron HTML, imágenes y scripts. Los enlaces llevan fuera de este sitio.

Ottosim — Simulation MCP Server

Give an AI assistant first-order engineering physics on a real CAD model. This local stdio MCP server carries a STEP file through explicit meshing in Gmsh to a typed study in Elmer — steady and transient thermal, linear static structural, modal, DC conduction, and laminar flow — and returns revisioned results, with no cloud service and no prepared solver input.

Every engineering input is explicit: the materials, boundaries, and mesh are stated, never inferred, and each study, mesh, and result is revision-bound so a number can always be traced back to what produced it.

Qualified against analytic references. Gmsh 4.15.2 and Elmer (devel, most recently released as 26.2.1) complete production-SIF qualifications for every advertised physics — steady and transient thermal, static structural, modal frequency, DC potential, and steady and transient laminar flow — each against an analytic reference with mesh (or timestep) refinement.

Runs on Node 26 / TypeScript 7 against MCP SDK 2.4.0, with a Python Gmsh worker over a one-request/one-response JSON-file protocol (protocol: 1).

Install and launch

Distribution targets match Geometry MCP: Windows x64, macOS ARM64 (Apple Silicon), and Linux x64. Intel macOS, Linux ARM64, and Windows ARM64 are outside the target matrix. CI runs unit/artifact/process checks on these three targets, builds the pinned serial Elmer solver on each target, caches the native install, runs the analytic integration and qualification suites, then packs and smoke-tests the published tarball. This validates native execution; a standalone runtime package is not yet published.

The repository owns its artifact schema in contracts/. Builds bundle that schema and the Python worker; Gmsh and Elmer runtimes are installed separately.

From this directory:

nix develop

just setup just build export SIMULATION_INPUT_ROOTS='["/absolute/path/to/models"]' export SIMULATION_WORKSPACE=/absolute/path/to/run-workspace just start

.envrc already contains use flake; direnv allow is an alternative to nix develop. .env.example documents variables; .env is not auto-loaded. just setup installs npm dependencies without lifecycle scripts. The flake pins nixpkgs (and thus Gmsh) by commit, and pins upstream Elmer by commit. Elmer's transitive inputs use its checked-in lock.

Nix provisions the solver toolchain only on x86_64-linux. packages. .toolchain provisions Node, Gmsh, the Python wrapper and headless upstream Elmer built serially without MPI, OpenMP, ElmerIce or Lua. The analytic thermal benchmark passes with that serial solver. The dev shell also provisions Python, Ruff and Nix tooling; TypeScript language server/formatter/linter/test deps are project-local npm dependencies. Gmsh's Python module lives under its Nix package's lib, supplied through PYTHONPATH. Elmer is an external package, not compiled by npm.

Non-Nix deployments must provide compatible Gmsh Python bindings and native library plus ElmerGrid and ElmerSolver with HeatSolve, FluxSolver, SaveData and ResultOutputSolve modules. Executables are operator configuration:

VariableDefault / meaning
SIMULATION_INPUT_ROOTSRequired JSON array of existing absolute input roots
SIMULATION_WORKSPACE.simulation-runs; local run/artifact storage
SIMULATION_PYTHONpython3 (Windows: python); Nix sets its wrapper
SIMULATION_ELMERGRIDElmerGrid
SIMULATION_ELMERSOLVERElmerSolver
SIMULATION_TIMEOUT_MS120000 per worker/conversion/solve; maximum 3600000

Generic host configuration example (environment may instead be inherited from the Nix shell):

{

"command": "node", "args": ["/absolute/path/to/simulation/dist/src/index.js"], "env": { "SIMULATION_INPUT_ROOTS": "[\"/absolute/path/to/models\"]", "SIMULATION_WORKSPACE": "/absolute/path/to/run-workspace", "SIMULATION_PYTHON": "/absolute/path/to/provisioned/simulation-python", "SIMULATION_ELMERGRID": "/absolute/path/to/ElmerGrid", "SIMULATION_ELMERSOLVER": "/absolute/path/to/ElmerSolver" } }

Study tools

The public workflow is get_capabilities → inspect_model → query_model → validate_study → prepare_study → run_study → inspect_job → query_results. list_jobs recovers recent jobs after context loss and cancel_job stops an active preparation or solver process.

inspect_job accepts waitMs (up to 60 s) and then waits in-process for a terminal state instead of returning immediately, so prepare_study → inspect_job(waitMs) → run_study → inspect_job(waitMs) → query_results is normally five calls rather than dozens of polls. The state is returned whether or not the window expires. A cancelled or timed-out client request stops the mesh or the solve: ctx.signal is forwarded to the job's abort path and the record ends cancelled. Preparation and solver execution return job IDs immediately. Jobs survive client disconnects, not server shutdown. Interrupted jobs are recorded and are not resumed.

get_capabilities reports the available physics and the study shape (required and optional fields, notes, and a worked example); the machine-readable simulation://help resource carries the full study schema — per-physics material and boundary fields with units, regimes, observation quantities with time/mode selectors, enums, and a parseable example. Both are generated from one catalog so discovery cannot drift from the accepted study schema.

steady-thermal, transient-thermal, static-structural, modal, dc-conduction, and laminar-flow are implemented and benchmarked. Other physics reported by get_capabilities remain unavailable, not implied by the fact that Elmer has upstream solvers for them.

Transient studies require explicit density and heat capacity per solid, one uniform initial temperature, a positive timestep, and a step count capped at 10,000. Fixed-temperature, insulated and inward-heat-flux boundaries are supported; time-varying conditions and per-region initial temperatures are not. Transient scalar and VTU results are validated at every step. An observation may select a positive timestep-aligned time in seconds; without time, it observes the final step. Acceptance thresholds remain in the quantity's units (for example, K), not seconds.

Static structural studies require an explicit isotropic Young's modulus and Poisson ratio for every solid, and an explicit fixed-displacement, prescribed-displacement, uniform-traction, or free condition for every surface. The supported model is static, linear-elastic, and small-strain; displacement and stress summaries are unweighted nodal samples, not volume averages.

Modal studies require explicit density in addition to elastic properties, fixed-displacement/free surface assignments, and request 1–20 modes. The first modal implementation reports eigenfrequencies only; it does not expose mode shapes, damping, prestress, or harmonic response. Modal observations select a mode number and use Hz.

DC conduction studies require explicit positive isotropic electrical conductivity per solid, explicit prescribed-potential or insulated conditions on every surface, and at least two distinct prescribed potentials per disconnected conductive component. Results report the nodal potential field; current, Joule heating, magnetic effects, and transient/capacitive behavior are not modeled.

Laminar-flow studies model incompressible Newtonian fluids with explicitly assigned constant density and dynamic viscosity. Every fluid surface must be explicitly assigned either a prescribed pressure or a no-slip wall; each disconnected fluid region needs at least two distinct prescribed pressures. Steady and transient runs are supported; transient runs use first-order BDF, constant timestep, and an explicit quiescent initial velocity. The solver is Elmer's monolithic FlowSolve module; qualification covers pressure-driven square-duct flow and transient startup against the analytic rectangular-duct solution, inlet/outlet flux conservation, and mesh/timestep refinement. Flux and section averages are not exposed; reported velocity and pressure values are nodal extrema. Elmer's segregated IncompressibleNS module is installed but not qualified. Turbulence, compressibility, moving walls, nonzero initial velocity, and thermal/electric coupling are not modeled.

Selections use dimension-qualified inspection entity IDs (for example 3:1 for a volume or 2:7 for a surface), have unique names and cannot mix solids and surfaces. Every inspected entity must be assigned exactly once. Each solid selection needs one material; every surface selection defaults to insulated in thermal studies unless explicitly overridden. Structural, modal, and DC studies require explicit conditions on every surface. A thermal default is a declared engineering assumption, not an inferred boundary condition. The solver uses SI units, but study quantities may be supplied in compatible engineering units (for example mm, in, psi, ksi, lbf, degC) and are converted at the API boundary. Mesh sizes default to metres and accept mm, cm, in or ft through mesh.units. Results are SI by default; an optional displayUnits block (for example { "length": "in", "pressure": "ksi" }) returns observations in the engineer's units. Linear tetrahedra are still the only supported element type.

Material entries may carry an optional reference with the designation, source name/revision, and basis returned by a reference tool such as Standards. The study preserves this caller-supplied context alongside the explicit numeric properties; Simulation does not fetch or verify the source. A typical value remains distinct from a qualified design allowable.

Example steady thermal study after inspecting the STEP file:

{

"schemaVersion": "1", "name": "Bar conduction", "objective": "Estimate the steady temperature and heat flux", "model": { "source": { "revision": "sha256:<64 lowercase hex digits>", "uri": "file:///.../source.step", "format": "step", "units": "mm" }, "inspection": { "revision": "sha256:<64 lowercase hex digits>", "uri": "file:///.../inspection.json", "format": "json" } }, "selections": [ { "name": "bar", "entities": ["3:1"] }, { "name": "hot_end", "entities": ["2:2"] }, { "name": "cold_end", "entities": ["2:3"] }, { "name": "other_faces", "entities": ["2:4", "2:5", "2:6", "2:7"] } ], "mesh": { "minSize": 0.003, "maxSize": 0.006, "elementOrder": 1 }, "physics": { "kind": "steady-thermal", "defaultBoundary": "insulated", "materials": [ { "selection": "bar", "conductivity": { "value": 10, "units": "W/(m.K)" } } ], "sources": [], "boundaries": [ { "selection": "hot_end", "kind": "temperature", "temperature": { "value": 400, "units": "K" } }, { "selection": "cold_end", "kind": "temperature", "temperature": { "value": 300, "units": "K" } } ] }, "observations": [{ "id": "max_temperature", "quantity": "temperature_max" }], "acceptanceCriteria": [ { "observationId": "max_temperature", "operator": "<", "threshold": { "value": 410, "units": "K" } } ], "assumptions": [] }

The example entity tags are illustrative; use tags from query_model and confirm each selection against the actual part. prepare_study returns a jobId; poll inspect_job until it succeeds, then pass its prepared artifact to run_study, which returns another jobId. query_results reports solver evidence sepa

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