feat(mcp): add get_print_statistics tool

This commit is contained in:
RNL
2026-08-19 22:11:51 +10:00
parent 1a28197eda
commit 0164a72598
2 changed files with 165 additions and 2 deletions

View File

@@ -9,7 +9,7 @@ from django.db.models import Avg, Count, ExpressionWrapper, F, FloatField, Q, Su
from django.utils import timezone
from bambu_run.mcp.shapes import display_name, normalize_consumed, resolve_printer
from bambu_run.models import BambuCloudTask, FilamentUsage, PrintJob
from bambu_run.models import BambuCloudTask, FilamentUsage, Filament, PrintJob
def list_print_jobs(
@@ -114,3 +114,95 @@ def get_print_job(job_id: int) -> dict:
"cloud_design_title": cloud.design_title if cloud else None,
"cover_url": cloud.cover_url if cloud and cloud.cover_url else None,
}
def get_print_statistics(days: int = 30, printer: str | int | None = None) -> dict:
"""Aggregate print analytics over a window: success rate, total time and
filament, estimate accuracy, failure patterns, and material mix.
cost_total is always null in this phase -- cost_available: false tells
the agent to explain the gap rather than report zero spend.
most_used_material answers "what did I print most" directly for the
requested window (days=7 for last week, days=30 for last month).
"""
since = timezone.now() - timedelta(days=days)
qs = PrintJob.objects.filter(start_time__gte=since)
if printer is not None:
qs = qs.filter(device__in=resolve_printer(printer))
total_jobs = qs.count()
status_counts = {
(row["final_status"] or "running"): row["n"]
for row in qs.values("final_status").annotate(n=Count("id"))
}
finished = status_counts.get("FINISH", 0)
success_rate = round(finished / total_jobs, 3) if total_jobs else None
total_duration = qs.aggregate(s=Sum("duration_minutes"))["s"] or 0
cloud_qs = BambuCloudTask.objects.filter(print_jobs__in=qs).distinct()
total_grams = cloud_qs.aggregate(s=Sum("weight_grams"))["s"]
jobs_without_cloud = total_jobs - cloud_qs.count()
# Same exclusion rules as normalize_consumed (starting_percent must be a
# real reading, and consumption must be non-negative), expressed as a DB
# filter -- a Python helper can't run inside an aggregate expression.
valid_usage = (
Q(starting_percent__gt=F("ending_percent"))
& ~Q(starting_percent=-1)
& Q(filament__initial_weight_grams__isnull=False)
)
material_rows = (
FilamentUsage.objects.filter(print_job__in=qs)
.filter(valid_usage)
.annotate(
grams=ExpressionWrapper(
(F("starting_percent") - F("ending_percent")) * F("filament__initial_weight_grams") / 100.0,
output_field=FloatField(),
)
)
.values("filament__type")
.annotate(total_grams=Sum("grams"))
.order_by("-total_grams")
)
material_mix = {row["filament__type"]: round(row["total_grams"], 2) for row in material_rows if row["filament__type"]}
most_used_material = next(iter(material_mix), None)
failed = qs.filter(final_status="FAILED")
failure_names = list(failed.values("project_name").annotate(n=Count("id")).order_by("-n")[:5])
ratio_qs = qs.filter(
duration_minutes__isnull=False, cloud_task__cost_time_seconds__gt=0
).annotate(
ratio=ExpressionWrapper(
F("duration_minutes") / (F("cloud_task__cost_time_seconds") / 60.0),
output_field=FloatField(),
)
)
mean_ratio = ratio_qs.aggregate(avg=Avg("ratio"))["avg"]
# median has no portable DB-level equivalent across backends; this list
# is already bounded by the same days/printer filters as everything else.
ratios = list(ratio_qs.values_list("ratio", flat=True))
median_ratio = statistics.median(ratios) if ratios else None
return {
"window_days": days,
"total_jobs": total_jobs,
"success_rate": success_rate,
"status_counts": status_counts,
"total_print_minutes": total_duration,
"total_filament_grams": float(total_grams) if total_grams is not None else None,
"jobs_without_cloud_data": jobs_without_cloud,
"estimate_accuracy": {
"mean_ratio": round(mean_ratio, 2) if mean_ratio is not None else None,
"median_ratio": round(median_ratio, 2) if median_ratio is not None else None,
},
"failures": {
"count": failed.count(),
"top_failing_projects": [{"name": f["project_name"], "count": f["n"]} for f in failure_names],
},
"material_mix_grams": material_mix,
"most_used_material": most_used_material,
"cost_total": None,
"cost_available": False,
}

View File

@@ -3,7 +3,7 @@ from datetime import timedelta
from django.utils import timezone
from bambu_run.models import Printer, PrintJob, BambuCloudTask, Filament, FilamentUsage
from bambu_run.mcp.jobs import list_print_jobs, get_print_job
from bambu_run.mcp.jobs import list_print_jobs, get_print_job, get_print_statistics
@pytest.mark.django_db
@@ -106,3 +106,74 @@ def test_get_print_job_computes_estimate_delta_percent():
def test_get_print_job_unknown_id_raises():
with pytest.raises(PrintJob.DoesNotExist):
get_print_job(999999)
@pytest.mark.django_db
def test_get_print_statistics_success_rate_and_status_counts():
p = Printer.objects.create(name="RNL-H2C", model="H2C", is_active=True)
now = timezone.now()
PrintJob.objects.create(device=p, project_name="A", start_time=now, final_status="FINISH")
PrintJob.objects.create(device=p, project_name="B", start_time=now, final_status="FAILED")
result = get_print_statistics(days=30)
assert result["total_jobs"] == 2
assert result["success_rate"] == 0.5
assert result["status_counts"]["FINISH"] == 1
assert result["status_counts"]["FAILED"] == 1
@pytest.mark.django_db
def test_get_print_statistics_material_mix_excludes_negative_consumption():
p = Printer.objects.create(name="RNL-H2C", model="H2C", is_active=True)
job = PrintJob.objects.create(device=p, project_name="A", start_time=timezone.now(), final_status="FINISH")
pla = Filament.objects.create(type="PLA", brand="Bambu Lab", color="Black", initial_weight_grams=1000)
abs_f = Filament.objects.create(type="ABS", brand="Bambu Lab", color="White", initial_weight_grams=1000)
FilamentUsage.objects.create(print_job=job, filament=pla, tray_id=0, starting_percent=74, ending_percent=68) # 6% -> 60g, valid
FilamentUsage.objects.create(print_job=job, filament=abs_f, tray_id=1, starting_percent=51, ending_percent=52) # jitter, excluded
result = get_print_statistics(days=30)
assert result["material_mix_grams"] == {"PLA": 60.0}
assert result["most_used_material"] == "PLA"
@pytest.mark.django_db
def test_get_print_statistics_estimate_accuracy_mean_and_median():
p = Printer.objects.create(name="RNL-H2C", model="H2C", is_active=True)
cloud1 = BambuCloudTask.objects.create(task_id=1, cost_time_seconds=100 * 60)
cloud2 = BambuCloudTask.objects.create(task_id=2, cost_time_seconds=100 * 60)
PrintJob.objects.create(
device=p, project_name="A", start_time=timezone.now(),
final_status="FINISH", cloud_task=cloud1, duration_minutes=110,
)
PrintJob.objects.create(
device=p, project_name="B", start_time=timezone.now(),
final_status="FINISH", cloud_task=cloud2, duration_minutes=130,
)
result = get_print_statistics(days=30)
assert result["estimate_accuracy"]["mean_ratio"] == 1.2
assert result["estimate_accuracy"]["median_ratio"] == 1.2
@pytest.mark.django_db
def test_get_print_statistics_cost_always_null_and_unavailable():
result = get_print_statistics(days=30)
assert result["cost_total"] is None
assert result["cost_available"] is False
@pytest.mark.django_db
def test_get_print_statistics_failures_lists_top_failing_projects():
p = Printer.objects.create(name="RNL-H2C", model="H2C", is_active=True)
now = timezone.now()
PrintJob.objects.create(device=p, project_name="Flaky", start_time=now, final_status="FAILED")
PrintJob.objects.create(device=p, project_name="Flaky", start_time=now, final_status="FAILED")
PrintJob.objects.create(device=p, project_name="Solid", start_time=now, final_status="FINISH")
result = get_print_statistics(days=30)
assert result["failures"]["count"] == 2
assert result["failures"]["top_failing_projects"][0] == {"name": "Flaky", "count": 2}