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time_impact_analysis_fragnet

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 baseline XER and impact is measured against that
        shared baseline. The result carries a
        ``single_base_disclosure`` warning explaining this is an AACE
        29R-03 §3.7 simplification — acceptable when all events share
        a single baseline window, but not strict MIP 3.7 Multiple
        Base.

        **Multi-base mode** (AACE 29R-03 MIP 3.7 Multiple Base):
        supply ``per_event_bases`` — a dict keyed by each fragnet's
        ``id``, with each value a dict containing EITHER
        ``xer_path`` OR ``xer_content`` for that event's
        pre-event contemporaneous baseline. Each fragnet is inserted
        into its OWN base, impact is measured against THAT base's
        pre-event finish, and the result carries
        ``per_event_methodology``, ``per_event_base_count``, and
        ``per_event_bases_used`` (sha256-truncated content hashes for
        audit reproducibility). The cumulative-impact figure carries
        ``cumulative_caveat`` because the sum of events measured
        against different bases is NOT a valid joint impact.

        Exactly ONE of {baseline_xer_path, baseline_xer_content,
        per_event_bases} must be supplied. Multi-base mode errors out
        (returning ``{"error": ...}``) if any fragnet id is missing
        from ``per_event_bases``.

        Use this tool when modeling delay impact prospectively (e.g.
        quantifying RFI / change-order delay before settlement). For
        retrospective windows analysis after the fact, use
        ``forensic_windows_analysis`` (MIP 3.3 windows).

        Args:
            baseline_xer_path:    server-side pre-impact baseline XER
                                  (single-base mode).
            baseline_xer_content: full text of pre-impact baseline XER
                                  (single-base mode, hosted/remote use).
            per_event_bases:      dict {fragnet_id: {"xer_path": "..."}
                                  OR {"xer_content": "<full XER text>"}}
                                  for AACE MIP 3.7 Multiple Base mode.
                                  Example::

                                    {
                                      "F1": {"xer_path": "/tmp/bl_pre_F1.xer"},
                                      "F2": {"xer_content": "<XER text>"},
                                    }

            fragnets: list of fragnet dicts. Each must have:
                - 'id', 'name', 'liability' (responsible party)
                - 'activities': list of {code, name, duration_days,
                                          clndr_id?}
                  (clndr_id is the P6 calendar id, CALENDAR.clndr_id.
                  This tool schedules on a uniform timeline that walks
                  no calendar, so it does not change the result here.)
                - 'ties':       list of {pred, succ, type, lag_days?}
                Optional: 'description'.
            output_dir: output dir for TIA_Report.txt + CSV (tempdir if "").
            project_name: optional override.

        Returns:
            {
              "report":      path to TIA_Report.txt,
              "impacts_csv": path to TIA_Impact_Details.csv,
              "baseline":    {"project_finish", "critical_count", ...},
              "per_fragnet": [{fragnet_id, name, liability,
                                completion_before, completion_after,
                                impact_days, impact_working_days,
                                affected_activities, status, error}, ...],
              "cumulative_days": int (sum of per-fragnet impacts),
              "cumulative_basis": str (BOTH modes — states the cumulative
                                  figure is the sum of independent
                                  per-fragnet impacts and overstates joint
                                  impact when fragnets share a path),
              "per_event_methodology": str (canonical label),
              "per_event_base_count": int (count of unique base XERs),
              "per_event_bases_used": {fragnet_id: sha256_hash8} (multi-base only),
              "single_base_disclosure": str (single-base only),
              "cumulative_caveat": str (multi-base only),
            }
        

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
fragnetsNo
output_dirNo
project_nameNo
per_event_basesNo
baseline_xer_pathNo
baseline_xer_contentNo

Schema Changelog

Changes observed during successful MCP inspections.

  1. Changed1 schema field changed
    • addedInput schema / properties / per_event_bases
      Added value: +{
      +  "additionalProperties": true,
      +  "default": null,
      +  "title": "Per Event Bases",
      +  "type": "object"
      +}
  2. First observed

TDQS

A5/5.0
Behavior5/5

Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?

With no annotations, the description carries the full burden and succeeds: it discloses output files, caveats (single_base_disclosure, cumulative_caveat), error behavior for missing fragnet ids, uniform-timeline scheduling behavior, and audit reproducibility via sha256-truncated content hashes. This goes well beyond what the input schema alone could convey.

Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.

Conciseness5/5

Is the description appropriately sized, front-loaded, and free of redundancy?

The description is long but efficiently organized by mode, arguments, and return fields. Every sentence carries load-bearing information such as constraints, caveats, or mode differentiation, and the structure makes the complex tool navigable rather than bloated.

Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.

Completeness5/5

Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?

For a 6-parameter tool with nested objects, no annotations, no output schema, and 0% schema coverage, this description is exceptionally complete. It covers mode selection, parameter semantics, error conditions, output structure, and methodological caveats, leaving no critical gap for an agent to call it correctly.

Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.

Parameters5/5

Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?

Schema description coverage is 0%, so the description must compensate, and it does fully. It explains every parameter, gives a concrete example for per_event_bases, documents the required fields inside each fragnet dict, and clarifies optional fields like clndr_id and lag_days. The agent has enough information to construct valid arguments.

Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.

Purpose5/5

Does the description clearly state what the tool does and how it differs from similar tools?

The description states a specific verb and resource: 'Time Impact Analysis (TIA) — prospective fragnet insertion into a pre-impact baseline schedule.' It clearly distinguishes its prospective use from the retrospective forensic_windows_analysis sibling tool, so an agent can select it correctly without opening the schema.

Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.

Usage Guidelines5/5

Does the description explain when to use this tool, when not to, or what alternatives exist?

It explicitly says 'Use this tool when modeling delay impact prospectively' and names the alternative: 'For retrospective windows analysis after the fact, use forensic_windows_analysis (MIP 3.3 windows).' It also explains when each mode applies (single-base vs multi-base) and the exact one-of constraint on baseline inputs.

Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.

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