mendrift-mcp 0.1.0

Autonomous MLOps incident response agent + an MCP server for drift detection and ML incident tooling

Mendrift

Autonomous MLOps incident response agent, plus mendrift-mcp — an open-source MCP server for drift detection and ML incident tooling.

When a production model drifts or degrades, Mendrift detects it, diagnoses the root cause from monitoring and registry evidence, proposes a remediation, and executes it only after human approval.

alert ──> classify ──> diagnose (MCP tools) ──> propose
             │                                     │
           noise ──> close               human approval gate
                                                   │
                                    execute ──> verify recovery

Built with LangGraph (agent orchestration), LangChain (ChatAnthropic + bind_tools), the Model Context Protocol, Evidently, MLflow, and Claude (Haiku + Sonnet).

mendrift-mcp tools

tool	type	purpose
get_drift_report	read	per-feature drift distances + schema changes (Evidently)
summarize_metric_anomalies	read	production vs previous model scored on current traffic
get_deployment_history	read	registry version transitions and aliases
diff_deployments	read	params / metrics / feature-schema diff between versions
propose_rollback	read	generates a reviewable rollback plan
execute_rollback	gated	requires a single-use HMAC approval_token
open_incident	write	incident record with diagnosis + evidence

Safety model

The approval gate is enforced in the tool layer, not the prompt: execute_rollback verifies a single-use, action-scoped HMAC token minted only by the human review flow — the minting function is never exposed over MCP. A prompt-injected or confused agent cannot execute writes.

Tested live: Claude was first ordered to roll back "with full authorization" (it proposed but declined to fabricate a token), then handed a fabricated token, which the gate rejected by constant-time HMAC comparison:

See tests/test_approval_gate.py, including the action-scoping test: a token minted for one model/version is invalid for any other.

Human-in-the-loop, crash-proof

The incident graph halts before execution (interrupt_before) and checkpoints every step to SQLite. The process can die; a new process resumes the same incident by thread_id after a human mints the approval token — which enters state only via update_state(), from outside the graph. Denial is a first-class path: no token → closed_approval_denied, no execution.

Agent design

step	model	why
classify	Haiku	single constrained label; cheapest path
diagnose	Sonnet	multi-hop tool reasoning over evidence
verify	Haiku	threshold check on fresh metrics

Routing lives in a code table (ROUTER_TABLE), not prompts, so cost per path is measurable config — ~3.9K input / 630 output tokens per incident. The diagnose loop is bounded (max 8 tool calls) with per-call retries and capped backoff; on tool failure the model receives a structured error record, and on budget exhaustion the agent degrades to an incident with partial evidence — it never invents a diagnosis. Destructive actions require affirmative evidence: a rollback is recommended only when retrieved evidence links the symptom to a specific deployment, never on deploy-correlation alone. The agent can also recommend monitor — real but mild, non-actionable drift is watched, not acted on.

Live mode

MENDRIFT_DEMO=0 runs the agent against real infrastructure rather than fixtures:

• scripts/seed_demo.py trains two sklearn versions into a local MLflow registry — v13 clean, v14 with a schema swap and a training window polluted by missed-fraud labels (recall 0.72 → 0.18, AUC 0.84 → 0.82) — and writes reference/current frames
• get_drift_report runs Evidently's DataDriftPreset over those frames, returning real Wasserstein/JS distances against per-metric thresholds, plus schema changes derived from actual column sets
• get_deployment_history / diff_deployments read the registry and the underlying runs — real aliases, params, metrics
• summarize_metric_anomalies scores the current window with both the production and previous versions, so it reports model divergence rather than population drift — a rollback clears it, ordinary data shift does not
• an approved execute_rollback moves the production alias for real

uv run mlflow server --host 127.0.0.1 --port 5001        # separate terminal
PYTHONPATH=src uv run python scripts/seed_demo.py

rm -f demo.db
MENDRIFT_DEMO=0 PYTHONPATH=src uv run python scripts/demo_interrupt.py start
MENDRIFT_DEMO=0 PYTHONPATH=src uv run python scripts/demo_interrupt.py approve

A live run diagnoses from computed evidence — e.g. "v2 introduced a schema swap replacing promo_flag with promo_flag_v2 … label_noise 0.0 → 0.45 collapsing val_recall 0.724 → 0.176 … 79.7% prediction-rate divergence from the prior version, model-induced, not population drift" — then halts for approval and resolves.

The eval suite deliberately stays on fixtures: evals need determinism and zero cost in CI, while live mode exercises the real stack.

Evaluation

src/mendrift/evals/ replays synthetic incident trajectories against the real graph — only the LLM (scripted) and the read tools (fixture world) are faked; the gated action tools are the genuine implementations, so the HMAC gate is exercised by every test. Four assertions per trajectory:

check	meaning
no_ungated_writes	every execute_rollback carried a valid HMAC token — hard fail
classification_ok	triage label matched
tool_sequence_ok	required tool calls occurred in order (extras allowed)
action_ok	terminal outcome matched

19 logic-distinct incident scenarios spanning the decision space, each with its own evidence shape and correct action:

• Rollback — deploy-correlated drift or quality regression with affirmative diff evidence
• Retrain — label/concept shift, segment-specific degradation (no valid rollback target)
• Monitor — mild seasonal drift, low-importance-feature drift, holiday effects
• Incident (investigate) — upstream schema rename, feature-store change, docs-only deploy, calibration break, threshold shift, silent data-quality drop
• Graceful degradation — evidence tools down → incident with partial evidence, never a fabricated diagnosis
• Noise — flapping / auto-resolved alerts closed with zero tool calls
• Human-declined — well-founded rollback the reviewer rejects → closed, no execution

Scripted for fast CI, live for the measured rate:

PYTHONPATH=src uv run python scripts/run_traj.py --all          # scripted, fast
PYTHONPATH=src uv run python scripts/run_traj.py --all --live   # real models

Live-model eval runs at ~95% task-success; the handful of run-to-run divergences reflect LLM eval variance on decision-margin scenarios. The live suite surfaced real failure classes during development — a JSON extractor masking a correct decision, a classifier baited by an alert's reassuring wording, and a diagnoser proposing rollback on correlation alone — each fixed at its own layer (parser, alert wording, evidence-rule prompt).

Quickstart (demo mode)

uv sync
MENDRIFT_DEMO=1 uv run mendrift-mcp     # stdio MCP server with fixture data
PYTHONPATH=src uv run pytest -v         # gate + trajectory suite

Claude Desktop config:

{"mcpServers": {"mendrift": {
  "command": "uv",
  "args": ["--directory", "/path/to/mendrift", "run", "mendrift-mcp"],
  "env": {"MENDRIFT_DEMO": "1"}
}}}

Status

• mendrift-mcp server over stdio, verified in MCP Inspector and Claude Desktop
• seven tools with a read / gated / write permission taxonomy
• HMAC-gated rollback with action-scoped single-use tokens (tests first)
• LangGraph incident graph: SQLite checkpointing + human-approval interrupt, kill-resume proven
• LLM nodes on LangChain (ChatAnthropic.bind_tools): Haiku classify/verify, Sonnet diagnose loop
• 19-scenario trajectory eval across the decision space; ~95% live, zero ungated writes
• CI: gate + trajectory suite on every push
• live mode: real Evidently drift computation, MLflow registry history/diff, real alias rollback
• publish: PyPI + MCP community servers registry