designsafe-mcp

An agent-first cyberinfrastructure layer for DesignSafe, scoped to a bounded subset of the quoFEM pipeline for geotechnical earthquake engineering with OpenSees as the forward solver. The server exposes scientific actions, find a tested workflow, build and validate a job, price it, run it after human approval, and package the result with provenance, so an AI agent (or a researcher) composes reproducible computational science instead of driving low-level APIs. supported_capabilities() declares exactly what is supported, in which scientific domain, and how much of it is tested; requests outside that map are declined by design.

dapi and Tapis remain the substrate; this layer translates research intent into trustworthy workflows. Orchestration comes from a tested snippet corpus (executed, self-checking, version-pinned examples), never from a model's recall of the API.

ARCHITECTURE.md records the full design: the layer stack, why decisions live in tools rather than in the model, deployment topologies, and the evaluation methodology.

Tools

Related MCP server: SciAgentKit

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 with claude 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 the expected decision; agent mode drives real models against the server in DESIGNSAFE_MCP_MOCK=1 mode (no Tapis calls, no SUs) and scores each trace on decision, grounding, and the approval gate. Pass rate per case per model is the ability metric.