forensic_windows_analysis
Run forensic windows analysis (AACE RP 29R-03 §3.3, MIP 3.3 Observational / Dynamic / Contemporaneous As-Is) across multiple Primavera P6 XER snapshots and return the full analysis dict.
This is the headline forensic tool — it computes per-window
completion shifts, per-window slip registers (per-activity slip
with a critical flag (total float of zero or less, or the driving
path in a schedule with negative float)), per-window duration
growth on critical-path activities, per-window per-party attribution
(Owner / Contractor / Concurrent / Force Majeure / Unattributed),
and cumulative project drift from baseline. The attribution math
satisfies the CPP conservation check, per the AACE 29R-03
§3.3.E.13 requirement that the summed per-period net impacts
equal the difference between the first schedule update and the
last schedule update used in the evaluation (per-party day
buckets sum to project drift within ±1 day, no cascade-double-
counting).
Use this tool for the full multi-window forensic claim. If you
already have a windows result and only want the per-window ×
per-party grid view, call ``concurrent_delay_matrix`` instead.
Args:
schedules: list of dicts in chronological order. Minimum 2
entries (baseline + at least one update). Each dict
must contain ``label`` (str) and EXACTLY ONE of:
- ``xer_path`` — server-side filesystem path, OR
- ``xer_content`` — full XER text content.
Use ``xer_content`` when calling a hosted MCP server
from a remote client whose XER lives locally.
project_name: optional override; auto-picked from XER if "".
baseline_idx: which entry in ``schedules`` is the contract
baseline (default 0 = first one).
entitlement_milestone: optional task_code (e.g.
"Ready for Takeover") — recorded on the result, not used
for math.
output_dir: optional dir for HTML dashboard / DOCX report.
If "", a tempdir is used and dropped after — the
dashboard / report paths in the response will point to
the temp location (caller responsible for moving them).
Returns:
{
"analysis": full dict from run_windows() with keys:
"windows", "cumulative", "baseline_label", "data_dates",
"attribution_summary", "mcpm_attribution", ...,
"dashboard": path to HTML dashboard (server-side),
"report": path to DOCX executive report (server-side),
"baseline_stability": {"worst_severity", "has_block", ...},
"data_date_corrections": the record of each update whose
data date was moved to its latest actual, and of a
baseline with actual dates after its data date
([] on clean files); each is also a data-quality
warning
"analysis"."criticality_basis": the schedules read by
their driving path because they have negative float,
and those beside them ([] when no schedule has
negative float); the box they write is one
data-quality warning
}
On failure: {"error": "..."} with no schedules processed.
Input Schema
TableJSON Schema
| Name | Required | Description | Default |
|---|---|---|---|
| schedules | Yes | ||
| output_dir | No | ||
| baseline_idx | No | ||
| project_name | No | ||
| entitlement_milestone | No |