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by io.github.danafitkowski
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monte_carlo_p50_p80

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 iteration, every activity duration is
        sampled from the chosen distribution (Triangular, BetaPERT,
        Uniform, Lognormal, etc.) parameterized by % of baseline
        duration; CPM re-runs and the project finish date is recorded.
        After all iterations, P10/P50/P80/P90 completion dates and a
        sensitivity tornado (per-activity correlation to project
        finish) are reported.

        Use this tool when you need probabilistic completion forecasts
        or a tornado/sensitivity ranking. For the QRAMM-aligned
        five-level maturity badge (AACE 122R-22) on the result,
        pipe the response into
        ``qramm_maturity``.

        Args:
            xer_path: server-side path to the schedule XER.
            xer_content: full text of the schedule XER (alternative for
                hosted/remote use). Supply EXACTLY ONE of path/content.
            iterations: number of MC iterations (default 5000).
            distribution: 'Triangular', 'BetaPERT', 'Uniform',
                'Lognormal' (case-insensitive — passed through).
            optimistic_pct, most_likely_pct, pessimistic_pct: %
                of baseline duration for the distribution params
                (defaults: 85 / 100 / 120).
            seed: optional fixed seed for reproducibility (0 = system
                entropy = non-reproducible).
            output_dir: optional output dir; tempdir if "".

        Returns:
            Full SRA result dict, key paths:
              - 'baseline.percentiles': lowercase p-keys
                {'p10','p25','p50','p75','p80','p85','p90','p95'},
                each {'day', 'date'}. NOTE: keys are lowercase — read
                result['baseline']['percentiles']['p80'], not 'P80'.
              - 'baseline.config':      sim params used
              - 'baseline.sensitivity': per-activity tornado rows
              - 'risk_register_simulation.percentiles' (only when a
                risk_register is supplied): SAME lowercase convention,
                {'p10','p50','p80','p90'} each {'day', 'date'}.
              - 'project_name', 'data_date', ...
              - HTML / DOCX paths if outputs emitted

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
seedNo
xer_pathNo
iterationsNo
output_dirNo
xer_contentNo
distributionNoTriangular
optimistic_pctNo
most_likely_pctNo
pessimistic_pctNo

Schema Changelog

Changes observed during successful MCP inspections.

  1. First observed

TDQS

A5/5.0
Behavior5/5

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

No annotations are provided, so the description carries the full burden. It discloses the simulation process (sampling, CPM re-runs, record finish dates), the lowercase key convention in output (e.g., 'p80' not 'P80'), the requirement to supply exactly one of xer_path/xer_content, and distribution parameterization. These are non-obvious behavioral details that help avoid misuse.

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?

Although the description is long, it is well-structured with sections for purpose, args, and returns. The first sentences immediately convey the tool's core purpose. Each sentence adds value, including the crucial key-format caveat and the alternative input methods. There is no redundant fluff.

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?

With 9 parameters, no output schema, and no annotations, the description is exceptionally complete. It details all inputs, explains the output structure with concrete key paths and conventions, and even notes conditional outputs (risk_register_simulation). It also covers edge cases like the path/content exclusivity and seed behavior.

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%, but the description explains every parameter: xer_path, xer_content (with exclusivity), iterations, distribution (with case-insensitivity), the three percentage parameters with defaults, seed (with 0 meaning non-reproducible), and output_dir. This fully compensates for the lack of schema descriptions.

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 explicitly states 'Monte Carlo Schedule Risk Analysis — P10/P50/P80/P90 completion-date forecast' and mentions sensitivity tornado ranking. It clearly distinguishes this tool from siblings (e.g., critical_path_validator, deterministic schedule tools) by focusing on probabilistic outcomes.

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?

The description provides direct guidance: 'Use this tool when you need probabilistic completion forecasts or a tornado/sensitivity ranking.' It also names an alternative for a related use case: pipe the response into qramm_maturity for a QRAMM-aligned maturity badge, and implies deterministic analysis is not its purpose.

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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