designsafe-mcp
An agent-first cyberinfrastructure layer for DesignSafe, scoped to abounded subset of the quoFEM pipeline for geotechnical earthquakeengineering with OpenSees as the forward solver. The server exposesscientific actions, find a tested workflow, build and validate a job, priceit, run it after human approval, and package the result with provenance,so an AI agent (or a researcher) composes reproducible computational scienceinstead of driving low-level APIs. supported_capabilities() declaresexactly what is supported, in which scientific domain, and how much ofit is tested; requests outside that map are declined by design.
dapi and Tapis remain the substrate; this layer translates research intentinto trustworthy workflows. Orchestration comes from a tested snippet corpus(executed, self-checking, version-pinned examples), never from a model'srecall of the API.
ARCHITECTURE.md records the full design: the layer stack, why decisionslive in tools rather than in the model, deployment topologies, and theevaluation methodology.
Tools
| Action | Tool |
|---|---|
| Decide how to run it | plan_simulation(request, facts...) walks the OpenSees decision matrix |
| See the matrix itself | opensees_matrix(); the deck's slide ships as an MCP resource |
| Find a tested workflow | search_snippets(query) |
| Search the corpus | search_community(query) over UW community data, dapi examples, the ds-workflows book |
| Ground in the real app interface | describe_app(app_id) |
| Move data | stage_inputs(local_dir) |
| Construct the experiment | build_job_request(...), build_workflow_preview(...) |
| Check before spending | validate_job(job), estimate_cost(job) |
| Human gate | approve_submission(job) -> token; submit_job(job, token) refuses without it |
| Execute and monitor | submit_job, job_status, get_results |
| Provenance | write_manifest(...) |
Run
uv venv .venv && uv pip install -p .venv/bin/python -e .
.venv/bin/python -m designsafe_mcp.server # stdio MCP server
Register it once for Claude Code withclaude mcp add designsafe -- $PWD/.venv/bin/python -m designsafe_mcp.server,or add the same command to .jupyter/mcp_settings.json for jupyter-ai.Auth rides on dapi's environment; nothing is stored here.
demo/transcript.txt holds an executed transcript: discovery, grounding,staging, validation, cost, a refused unapproved submission, the approved run,results, the manifest, and a compiled two-stage DAG preview.
Evals
.venv/bin/python evals/runner.py --mode planner # deterministic floor
.venv/bin/python evals/runner.py --mode agent --models haiku,sonnet --trials 3
Golden cases in evals/cases.yaml map natural-language requests to theexpected decision; agent mode drives real models against the server inDESIGNSAFE_MCP_MOCK=1 mode (no Tapis calls, no SUs) and scores eachtrace on decision, grounding, and the approval gate. Pass rate per caseper model is the ability metric.
