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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,
                                          calendar_id?}
                - '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

TDQS

A4.9/5.0
Behavior5/5

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

No annotations are present, so the description carries the full burden. It discloses two modes, error conditions (missing fragnet IDs in per_event_bases return an error), and important caveats such as cumulative_basis overstating joint impact and cumulative_caveat for multi-base mode. This is exceptionally transparent.

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

Conciseness4/5

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

The description is long, but it is well-structured with clear mode headers, Args, and Returns sections. Every sentence provides value; while not maximally concise, the complexity of the tool justifies the length.

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?

Given the tool's complexity (6 parameters, nested objects, no output schema), the description is remarkably complete. It covers modes, arguments, return values, error behavior, caveats, and provides an example. The agent has everything needed to invoke 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 compensates fully. The Args section explains every parameter, including structure for per_event_bases and fragnets, with an example dict. This goes beyond the bare schema.

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 opens with 'Time Impact Analysis (TIA) — prospective fragnet insertion into a pre-impact baseline schedule,' providing a specific verb and resource. It clearly distinguishes itself from sibling forensic_windows_analysis by explicitly contrasting prospective versus retrospective analysis.

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?

Explicit guidance is given: 'Use this tool when modeling delay impact prospectively... For retrospective windows analysis after the fact, use forensic_windows_analysis (MIP 3.3 windows).' This names the alternative and specifies when-not-to-use.

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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TDQS

A4.7/5.0
Disambiguation4/5

Each tool targets a distinct analytical deliverable (windows analysis, concurrency matrix, slip velocity, TIA, collapsed as-built, SRA, etc.), and descriptions explicitly cross-reference sibling tools to clarify boundaries. Some pairs like critical_path_validator vs dcma14_health_check and forensic_windows_analysis vs concurrent_delay_matrix share inputs and close conceptual territory, but the stated distinctions are clear enough to prevent misselection.

Naming Consistency4/5

All tool names follow a consistent lowercase snake_case style with descriptive noun phrases (e.g., forensic_windows_analysis, slip_velocity, xer_parser), so there is no mixing of conventions. However, the pattern is not verb_noun and a few names embed acronyms or numbers (dcma14_health_check, monte_carlo_p50_p80, woet_classifier), which is a minor deviation from a fully uniform naming scheme.

Tool Count5/5

13 tools is well-scoped for a forensic CPM/schedule delay analysis server. Each tool covers a distinct method or deliverable—parsing, logic health, DCMA-14, windows analysis, concurrency, slip trends, TIA, collapsed as-built, Monte Carlo SRA, maturity assessment, WOET, path tracing, and an evidence workbench—so every tool earns its place without redundancy.

Completeness5/5

The tool surface comprehensively covers the forensic delay analysis lifecycle: input parsing, schedule logic validation, DCMA-14 health assessment, retrospective windows analysis, concurrency attribution, slip trending, prospective TIA, collapsed as-built, probabilistic SRA, maturity rating, execution classification, and raw-evidence workbench. Minor gaps like schedule editing or cost analysis exist but are outside the server's stated forensic-analysis purpose.