MCP server

Critical Path Partners

io.github.danafitkowski/cpp-cpm-engine

Checked 25 Oct 2026, 20:29 UTC · Connected to it and listed its tools

Forensic scheduling MCP for Primavera P6 (XER): AACE windows, DCMA-14, Monte Carlo, TIA.

Website Source

At a glance

Type

MCP server

Publisher

Not verified

Status

Working at last check

Tools

13

Token cost

About 9,433 tokens per request

Sets up in

Claude Code, Claude Desktop, Codex, Gemini CLI, Cursor, VS Code, Antigravity, ChatGPT, OpenAI API, Grok

Last checked

25 Oct 2026, 20:29 UTC

Connect it to your agent

Add to CursorAdd to VS CodeVS Code Insiders

Opens the app, which shows the server and asks you to confirm. For other apps, copy the setup below.

Claude Code

claude mcp add --transport http cpp-cpm-engine https://mcp.criticalpathpartners.ca/mcp

Claude Desktop

Settings → Connectors → Add custom connector, and paste the URL.

Codex (config.toml)

[mcp_servers.cpp-cpm-engine]
url = "https://mcp.criticalpathpartners.ca/mcp"

Gemini CLI

{"mcpServers":{"cpp-cpm-engine":{"httpUrl":"https://mcp.criticalpathpartners.ca/mcp"}}}

Cursor

{"mcpServers":{"cpp-cpm-engine":{"url":"https://mcp.criticalpathpartners.ca/mcp"}}}

VS Code

{"servers":{"cpp-cpm-engine":{"type":"http","url":"https://mcp.criticalpathpartners.ca/mcp"}}}

Antigravity

agy mcp add cpp-cpm-engine https://mcp.criticalpathpartners.ca/mcp

ChatGPT

Settings → Apps & Connectors → Advanced settings: turn on Developer mode, then Create a connector and paste the URL. Which plans can do this varies.

OpenAI API (Responses tools)

{"type":"mcp","server_label":"cpp-cpm-engine","server_url":"https://mcp.criticalpathpartners.ca/mcp","require_approval":"always"}

Grok (xAI API tools)

{"type":"mcp","server_label":"cpp-cpm-engine","server_url":"https://mcp.criticalpathpartners.ca/mcp"}

Tools

• claim_workbench_evidence_ledger ~790 tokens
  Forensic claim workbench — analyzes a folder of mixed evidence (XER chain + MSG/PDF/DOCX/XLSX correspondence) and produces a unified workbench dashboard. Built from the real-world workflow where forensic delay analysis starts from a folder containing
• collapsed_as_built ~787 tokens
  Collapsed As-Built / But-For analysis on a post-impact XER. Implements AACE RP 29R-03 §3.8 Modeled / Subtractive / Single Base method (paired with MIP 3.3 Windows for the dual-method gap report per SCL §11.5). Validates a forensic windows analysis (MI
• concurrent_delay_matrix ~710 tokens
  Build the per-window x per-party concurrent-delay attribution matrix from a chronological list of XER snapshots. Implements the per-window concurrency view per AACE RP 29R-03 §3.3.I (apportionment) and §4.2 (concurrency). Where ``forensic_windows_anal
• critical_path_validator ~746 tokens
  Critical-path validation, logic health, and DCMA-14 assessment of a Primavera P6 schedule. Runs the CPP critical-path validator: checks for false criticality, constraint-driven CP segments, open ends, broken logic, and surfaces a DCMA-14 block with th
• dcma14_health_check ~795 tokens
  Full Schedule Health Dashboard HTML report — DCMA-14 + CPLI + BEI + variance/slip register against the baseline. Wraps the CPP Schedule Health Review skill, which produces a self-contained ~1.3 MB HTML dashboard. The dashboard renders DCMA metrics, ch
• forensic_windows_analysis ~743 tokens
  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
• monte_carlo_p50_p80 ~798 tokens
  Monte Carlo Schedule Risk Analysis — P10/P50/P80/P90 completion-date forecast for a Primavera P6 schedule. Implements an AACE-style quantitative SRA (the same math as CPP's browser Tool_11 Portfolio Risk Engine, scripted Python counterpart). For each
• path_explorer ~674 tokens
  Logic-trace driver-chain explorer — answers "WHY is this activity critical?" and "WHAT does it drive?". Traces driving predecessors backward from a target activity to project start (the "why critical" chain) and/or driving successors forward to projec
• qramm_maturity ~654 tokens
  QRAMM-aligned maturity reading for an SRA result. Places a Schedule Risk Analysis run (from ``monte_carlo_p50_p80`` or any equivalent dict) on the five named maturity levels of AACE RP 122R-22 (Quantitative Risk Analysis Maturity Model), section 3: le
• slip_velocity ~703 tokens
  Per-window slip velocity & acceleration trend across XER snapshots. Computes three signed metrics per window from the underlying forensic windows analysis: - slip_velocity_days_per_day: completion shift / window duration (positive = slipping, ne
• time_impact_analysis_fragnet ~774 tokens
  Time Impact Analysis (TIA) — prospective fragnet insertion into a pre-impact baseline schedule. Supports two modes. **Single-base mode** (legacy): supply ``baseline_xer_path`` or ``baseline_xer_content``. All fragnets are inserted into the same shared
• woet_classifier ~727 tokens
  Worked-vs-On-time Execution Timeline (WOET) per-activity day-by-day classification of as-built execution against baseline. For each pairable activity (matched by ``task_code``), classifies execution into 4 day-states: - PROGRESS: work performed duri
• xer_parser ~532 tokens
  Parse a Primavera P6 XER file and return a TABLE SUMMARY (not the full row-level data — XER row dumps explode the MCP context window). For each table in the XER, returns the table name, field list, and record count. Per-row data is intentionally omitted — for

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