OpenPASO MCP Server

by open-PASO• 29•• Scanned•Claim this listing•Request removal
Learning Resources4c multiphysicsai agentscomputational mechanicsdealiifebio

openPASO (formerly OASiS): an MCP server that lets an AI model run nine finite-element and multiphysics codes, with curated knowledge, verified execution and cross-code coupling. Under active development; contributions welcome.

OpenPASO is an open-source mcp server by open-PASO, written mainly in Python, with 29 GitHub stars and the MIT license. Its code was last updated Oct 15, 2026.

Scan summary: scanned Nov 5, 2026 at commit fd19a85. Seen able to run shell commands, touch money or trading. Scan policy

Scanned
Full scan at fd19a85 · Nov 5, 2026
Scanned Builds Boots No undeclared egress Reproducible
Static code scanwarn
Secrets scanpass
Dependency audit157 known vulnerabilitiesfail
Manifestnonewarn
Buildpass
Boot & handshake25 toolspass
Network egresspass
Reproducible (×3)pass

Findings (16)

  • high
    dependencies issue
    Details are available to the maintainer after claiming this listing.
  • high
    dependencies issue
    Details are available to the maintainer after claiming this listing.
  • high
    dependencies issue
    Details are available to the maintainer after claiming this listing.
  • medium
    Runs shell commands. Fine for some tools, but it means the agent can run anything your user can.
    src/core/source_discovery.py:302
  • medium
    httpcore2@2.9.1: 2 known vulnerabilities
    PYSEC-2026-3844, PYSEC-2026-3844
    langgraph_eval/requirements-langgraph.txt
  • medium
    httpx2@2.9.1: 10 known vulnerabilities
    PYSEC-2026-3845, PYSEC-2026-3846, PYSEC-2026-3847, PYSEC-2026-3848, PYSEC-2026-3849 +5 more
    langgraph_eval/requirements-langgraph.txt
  • medium
    python-multipart@0.0.9: 8 known vulnerabilities
    PYSEC-2026-1851, PYSEC-2026-1852, PYSEC-2026-3036, PYSEC-2026-3037, PYSEC-2026-3038 +3 more
    langgraph_eval/requirements-langgraph.txt
  • medium
    torch@2.7.0: 14 known vulnerabilities
    PYSEC-2025-203, PYSEC-2025-204, PYSEC-2025-205, PYSEC-2025-206, PYSEC-2025-207 +9 more
    langgraph_eval/requirements-vllm.txt
  • medium
    transformers@5.9.0: 1 known vulnerability
    PYSEC-2026-3929
    langgraph_eval/requirements-vllm.txt
  • medium
    xgrammar@0.1.19: 2 known vulnerabilities
    PYSEC-2026-2054, PYSEC-2026-2322
    langgraph_eval/requirements-vllm.txt
  • medium
    source-map-js@1.2.1: 1 known vulnerability
    GHSA-68fv-2mgg-jv7q
    ui/package-lock.json
  • medium
    vite@8.3.0: 6 known vulnerabilities
    GHSA-587h-7wmj-h5pm, GHSA-698v-4r3x-3vcv, GHSA-9jrq-w75r-8gcw, GHSA-r825-w8mm-5pxw, GHSA-rq7h-c2jc-7f22 +1 more
    ui/package-lock.json
  • low
    diskcache@5.6.3: 1 known vulnerability
    PYSEC-2026-2447
    langgraph_eval/requirements-vllm.txt
  • low
    scipy@1.6.1: 2 known vulnerabilities
    PYSEC-2023-102, PYSEC-2023-114
    langgraph_eval/requirements-vllm.txt
  • low
    katex@0.16.47: 1 known vulnerability
    GHSA-238p-pmpm-9mq7
    ui/package-lock.json
  • low
    No manifest declaring network access and environment variables (agent.json)

Network activity

During install
files.pythonhosted.orgpypi.org
While running
No connections

Tools exposed (25)

  • knowledge

    Get knowledge about solvers, physics, materials, coupling, post-mortems, or input formats. START WITH THE INDEX when you want pitfalls and do not yet know what to ask for: `knowledge(topic='pitfalls', solver=..., index=True)` returns a one-screen map — how many entri

  • discover

    Discover available solvers and their capabilities. Args: query: What to discover. Options: - "list" — list all solvers with status - "physics" — list all physics types per solver - "capabilities" — full capabilities matrix

  • examples

    Find and retrieve example input files from solver test suites. IMPORTANT: Always call this before writing new input files to study real, validated configurations. Args: keyword: Search term (e.g. 'peridynamic', 'fsi', 'poisson', 'heat') solver: Backe

  • submit_critic_review

    Put an independent critic's review of a setup ON RECORD, so a run of that setup can be verified. REQUIRED, and the most common mistake: pass EXACTLY ONE of `setup` or `coupling_args`. `setup` is the deck text for run_simulation / run_with_generator / verify_mesh_inde

  • run_with_generator

    Run a generator script that creates an input file, then execute the solver. Use this for solvers that need a COMPILED binary or separate input files: - 4C: generator creates .4C.yaml + mesh, then 4C binary runs on them - deal.II: generator creates main.cpp, then cmake + make

  • check_input

    Name the defects of an input deck or script BEFORE it runs: the setup checks the run tools apply, as a standalone gate for a code you run yourself (a coupling participant's own deck). For 4C every section name is judged by the INSTALLED binary's own grammar (`4C -p`) and the

  • run_simulation

    Run a simulation directly with input content. Use this for Python-based solvers (FEniCS, NGSolve, scikit-fem, DUNE-fem) where the input IS a Python script. The tool routes through the correct Python environment automatically (e.g., conda env for FEniCS). For 4C/deal

  • verify_pde_consistency

    Does your field actually satisfy the equation the task stated? The body is shared with couple(), which runs this same check on every converged level when it is handed the four arguments. Two wordings of the invitation to call this tool were measured and both failed -

  • verify_interface_flux

    On a COUPLED task: is your interface flux the right sign, and do your two sides actually agree there? Runs on YOUR OWN files, with no reference solution. THIS EXISTED ONLY INSIDE AN INDEPENDENT CHECK UNTIL NOW, which is why it is here. The check below is the one that

  • audit_results

    Check your OWN result files for the failures that most often sink a result set — BEFORE you hand it in. Uses only files you produced; no reference solution is involved, so a clean audit means self-consistent, not correct. What it catches, measured on 94 independently

  • verify_mesh_independence

    Heuristic mesh-independence study for problems WITHOUT an exact solution: re-run the SAME problem at successively refined resolutions and accept it as converged only if ALL monitored quantities stop changing materially. MMS convergence tests need a manufactured exact

  • coupled_solve

    LEGACY cross-solver coupling — FIXED toy geometries only. PREFER `couple`. DEPRECATED: this tool only handles a fixed enum of benchmark problems on a hardcoded unit-square split at x=0.5 (heat_dd/poisson_dd/one_way/tsi_dd/...). For ANY real or non-benchmark coupling use the

  • couple

    GENERAL partitioned multi-code coupling — works for ANY physics/coupling. Have an independent critic review the setup before coupling; pass critic_approved=True only after that review (every simulation must be critic-reviewed first). Unlike coupled_solve (legacy, fi

  • couple_levels

    EVERY PRESCRIBED MESH LEVEL IN ONE CALL -- the same partitioned coupling as `couple`, run once per level of a task's mesh sequence. Measured over three rounds of recorded runs: six couplings that were proven at level 1 (both codes ran, the iteration converged) never reached

  • write_participant_contract

    Write the served participant CONTRACT for `solver` to `path`, solve elided. The file is byte-for-byte the text `knowledge(topic='coupling', solver=..., signal='participant[:<variant>]:part<k>')` serves in parts, concatenated: the contract with its handshake, its checks and i

  • couple_precice

    GENERAL preCICE coupling of ARBITRARY codes/paradigms, end-to-end. Have an independent critic review the setup before coupling; pass critic_approved=True only after that review. The standard-library (preCICE) path for cross-code coupling — works for any number of pa

  • visualize

    Post-process and visualize simulation results. Args: job_id: Job ID from run_simulation (or leave empty and set work_dir) work_dir: Direct path to results directory action: What to do. Options: - "summary" — field statistics + results_summ

  • developer

    Developer tools: architecture, source files, capabilities matrix. Args: action: What to surface. Options: - "architecture" — extension points + source-tree layout for the requested solver - "files" — source-file listing filtered

  • prepare_simulation

    Prepare everything needed to set up a simulation — in ONE call. Returns: knowledge + real test file examples + generated template. This eliminates 3 separate tool calls before every simulation. Supports fuzzy matching: e.g. 'magnetostatics' finds 'maxwell', 'thermal

  • transfer_field

    Extract field values at an interface from a VTU file and format for transfer. Universal data connector for cross-solver coupling. Reads VTU output from any solver, extracts values at the interface plane, and formats them for the target solver's expected input shape.

  • generate_mesh

    Generate a mesh using Gmsh for non-trivial geometries. THE SHAPE IS YOURS TO SET. Each geometry below has DIMENSIONS, and `params` sets them. Without it you get this server's defaults, which are one particular published configuration and almost certainly not the one

  • reload_catalog

    Hot-reload the per-backend KNOWLEDGE dicts from disk. Closes a gap one of openPASO's own post-mortems recorded: the MCP server normally imports src/backends/<be>/generators/<physics>.py modules ONCE at startup and never refreshes them, so catalog edits made d

  • rediscover_backends

    Probe the system for solver backends and report findings. Searches pip packages, conda environments, common build directories, and source roots. In developer mode, reports git branch and status. Args: confirm: If True, save the discovered config for future sessi

  • setup_backend

    Guided backend setup: detect, plan, install, verify, persist. Helps a new user get any of the 8 FEM backends working on THIS machine — picking the fastest install route for the current OS (pip > conda > binary download > source build), executing it, running the backe

  • session_insights

    Review knowledge discovered during this session or from saved journals on disk. Two flows are supported: * In-session flow: call ``review`` -> ``approve_all`` / ``reject_all`` during the live MCP session to surface candidates from the current journal and

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