Alchemist-MCP
Click on "Deploy Server".
Wait a few minutes for the server to deploy. Once ready, it will show a "Started" state.
In the chat, type
@followed by the MCP server name and your instructions, e.g., "@Alchemist-MCPverify the Quantum ESPRESSO dataset and freeze it if all checks pass"
That's it! The server will respond to your query, and you can continue using it as needed.
Here is a step-by-step guide with screenshots.
⚗️ Alchemist-MCP
A fail-closed MCP verification layer that gates DFT-to-MLIP (MACE / RuNNer) migration-barrier workflows behind scientific, software, and numerical checks — so a broken calculation can never present itself as a valid one.
Why Alchemist-MCP?
Fail-closed by construction. Every tool returns only
PASS | WARN | FAIL | QUARANTINE | NOT_RUN— there is no code path that upgrades incomplete or unverified evidence into an apparent pass.Verification before training. MACE and RuNNer adapters can only ever build a dry-run
CommandPlan;execute=True/dry_run=Falseis refused unconditionally at the MCP boundary.Provenance-first data intake. Quantum ESPRESSO parsing preserves atom order, canonical units, pseudopotential hashes, and paired input/output SHA-256 identity, with fail-closed quarantine on anything incomplete or ambiguous.
Honest status reporting. The project distinguishes engineering validation (proven by tests) from real scientific validation (pending real DFT data) everywhere, including in this README.
Related MCP server: colabfit-mcp
Architecture
flowchart TD
A[Quantum ESPRESSO DFT] --> B[Scientific verification]
B --> C[Software verification]
C --> D[Numerical verification]
D --> E[Frozen dataset]
E --> F[MACE]
E --> G[RuNNer 2G]
F --> H[Common evaluation]
G --> H
H --> I[Reference / literature validation]What is validated today?
Area | Status | Evidence |
QE parsing, units, provenance, freeze/split | Engineering — validated | 227 automated tests |
MACE / RuNNer adapters (dry-run only) | Engineering — validated | 227 automated tests |
Common evaluator + reference-comparison contract | Engineering — validated | 227 automated tests |
MCP server (8 tools, official SDK client) | Engineering — validated | Live stdio client run |
rMD17 engineering trial (real, non-DFT dataset) | Engineering — validated |
|
Gate 1 — Scientific Correctness (real DFT data) | NOT_RUN | No production migration-barrier DFT dataset yet |
Gate 2 — Software Correctness | PASS | 227 tests, |
Gate 3 — Numerical Correctness (real data) | NOT_RUN | No production HPC data yet |
Gate 4 — Reference / Literature Validation | NOT_RUN | Blocked on Gates 1 and 3 |
Engineering validation proves the software does what it claims on real and synthetic inputs. It is not, and must not be read as, scientific validation of any migration-barrier result.
Validated Engineering Demo
A real, non-synthetic 175 MB rMD17 azobenzene dataset is exercised end to end, purely as a software/engineering trial:
SHA-256 verification
-> NPZ schema/finiteness validation
-> deterministic seeded selection (<= 1000 samples)
-> kcal/mol -> eV unit conversion
-> non-periodic extended-XYZ export
-> ASE read-back
-> dry-run MACE CommandPlan (never executed)Every output is hard-labeled trial_label = "ENGINEERING_TRIAL_ONLY" and scientific_gate_eligible = false: azobenzene has no defect, no migration path, and no QE provenance, so this trial can never satisfy Gate 1, Gate 3, Gate 4, or training_eligibility. Full commands and live run output: reports/engineering_demo_validation.md.
MCP Tools
Tool | Purpose | Fail-closed behavior |
| Lifecycle/gate snapshot | Never executes work |
| Read-only QE dataset inspection | Structured quarantine errors per file |
| Run Scientific + Software + Numerical checks | Missing evidence -> |
| Immutable, versioned manifest | Missing evidence/duplicates -> |
| MACE dry-run command plan |
|
| Al-only RuNNer dry-run command plan | Non-Al data or execution request -> |
| Energy/force/migration-profile comparison | Incomplete evidence -> |
| Compare against DFT CI-NEB + literature | Unverified evidence -> |
Quick Start
# clone and enter the repo
git clone <repo-url> alchemist-mcp && cd alchemist-mcp
# install the locked environment (Python >=3.11)
uv sync --extra dev --locked
# run the test suite
uv run pytest -q
# lint and type-check
uv run ruff check .
uv run mypy src
# start the MCP server over stdio
uv run python -m alchemist_mcp.serverVerification philosophy
Every gate resolves to exactly one of PASS | WARN | FAIL | QUARANTINE | NOT_RUN. There is no implicit success: missing, incomplete, or unverifiable evidence always resolves to NOT_RUN or QUARANTINE, never PASS. A critical FAIL blocks the next scientific action outright. Training (MACE/RuNNer) is gated behind Scientific, Software, and Numerical verification of the input data, and final scientific claims additionally require independent reference/literature validation and human review. Fixture and trial-dataset successes verify software contracts only — they never substitute for real scientific, numerical, or literature validation.
Project status
Current milestone: engineering-demo-v1 — the software pipeline, MCP surface, and a real (non-DFT) end-to-end engineering trial are implemented and fully tested. Real scientific Gates 1, 3, and 4 remain pending genuine production DFT evidence; no MACE or RuNNer training has been executed.
Documentation
architecture.md— system design and MCP tool surfacescientific_gates.md— the four-layer verification gate definitionsdefinition_of_done.md— phase-by-phase completion criteria and evidencereports/engineering_demo_validation.md— full engineering demo evidence and live run output
Current limitations
No production migration-barrier DFT dataset exists in this repository; Gates 1, 3, and 4 are
NOT_RUNfor real data.The rMD17 trial is engineering-only and cannot satisfy any scientific gate.
No MACE or RuNNer training has ever been executed, real or trial — only dry-run command construction.
The known legacy Al 2x2x2-vs-3x3x3 finite-size delta (
0.1105 eV) exceeds the versioned<= 0.03 eVGate 3 threshold: a documented Gate 3 FAIL, not a passing result.MCP Inspector (interactive dev tool) is unverified non-interactively; the official MCP Python SDK stdio client covers startup, discovery, schema, and invocation instead.
License
No license file is currently included in this repository. All rights reserved by the author unless a license is added.
Available Tools
8 toolscompare_modelsC
Compare model arrays only when ordered common-test record IDs match.
Inputs: record_ids (required), dft_energy_eV (required), mace_energy_eV (required), mace_record_ids (required), +11 more. Returns: object
freeze_verified_datasetC
Freeze only after Gate 1-3 eligibility is proven.
Inputs: seed, dry_run, records, dataset_path, +1 more. Returns: object
get_pipeline_statusA
Return lifecycle and gate status without expensive execution.
Inputs: no named parameters. Returns: object
inspect_dft_datasetB
Inspect QE outputs without mutating accepted data.
Inputs: dataset_path. Returns: object
run_mace_pipelineC
Return a fail-closed MACE dry-run plan; never launches MACE.
Inputs: mode, seed, dry_run, execute, +3 more. Returns: object
run_runner2_pipelineC
Return a fail-closed Al-only RuNNer 2G dry-run plan; never launches RuNNer.
Inputs: dry_run, execute, records, work_dir, +5 more. Returns: object
validate_referenceC
Validate barriers against supplied DFT/reference evidence without literature claims.
Inputs: literature, dft_provenance, runner2_barrier_eV, runner2_provenance, +5 more. Returns: object
verify_dft_datasetC
Run available fail-closed verification over incoming QE outputs.
Inputs: dataset_path. Returns: object
Tool Schema Changelog
Recent tool additions, removals, and schema changes observed during successful MCP inspections.
TDQS
Scored across 8 tools
Tools target distinct operations (status, inspect, verify, freeze, run dry-runs, compare, validate). However, run_mace_pipeline and run_runner2_pipeline are very similar in nature (dry-run plans for different models), and inspect_dft_dataset and verify_dft_dataset could be confused as both deal with QE outputs. Descriptions clarify the differences, so ambiguity is minimal.
All tool names follow a consistent verb_noun pattern (get_pipeline_status, inspect_dft_dataset, verify_dft_dataset, freeze_verified_dataset, run_mace_pipeline, run_runner2_pipeline, compare_models, validate_reference). Even multi-word nouns are handled uniformly, making the naming highly predictable.
Eight tools provide a well-scoped surface for a specialized DFT/MACE/RuNNer workflow. Each tool corresponds to a distinct step or check in the pipeline, and no tool appears redundant or excessive.
The tools cover inspection, verification, freezing, dry-run planning, comparison, and reference validation, but there are notable gaps: no tool to actually execute a pipeline (only dry-runs), no tool to retrieve or manage datasets beyond inspection, and no tool to handle pipeline failures or updates. These gaps could hinder agents from performing end-to-end workflows.
Maintenance
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