Returns the current date and time in the specified IANA timezone. Useful for grounding the LLM in the user's local time. Example timezones: 'UTC', 'America/New_York', 'Europe/London', 'Asia/Tokyo'.

Converts an ISO-8601 timestamp to a target IANA timezone. The input timestamp must include timezone information or be in UTC. Returns the localised time, UTC offset, and human-readable format.

Parses a human-readable duration string into structured seconds. Supports combinations of y (years), mo (months), w (weeks), d (days), h (hours), m (minutes), s (seconds). Examples: '2h30m', '1d6h', '1w3d', '45m'.

Performs compound time-math operations. Dispatches to the appropriate sub-operation based on the 'operation' parameter. Operations: add_days, add_business_days, diff, convert_tz, parse_nl, format_duration.

Adds N business (working) days to a start date, skipping weekends and country-specific public holidays. Supports US, UK, FR, DE, and JP holidays.

Counts the number of business (working) days between two dates, excluding weekends and country-specific public holidays. The count is exclusive of the start date and inclusive of the end date.

Calculate PERT expected duration from three-point estimates using Beta distribution.

Formula: E = (O + 4M + P) / 6. Returns expected value, variance, standard deviation, and 95%/99% confidence bounds with urgency categorization. Use when estimating task duration with uncertain outcomes.

LLM-adapted COCOMO II parametric effort estimation.

Replaces traditional 17 human-labor cost drivers with 5 LLM-specific factors: reasoning complexity, context completeness, transformation impact, iterative cycles, and human oversight. Returns both nominal and LLM-adjusted person-months.

Forecast sprint completion date from backlog size and historical velocity.

Computes average velocity from sprint history, converts story points to hours, and returns required sprints with pessimistic estimate based on velocity variance.

Compute critical path with merge-bias adjustment for project schedules.

Performs forward/backward pass to identify critical tasks and slack. Applies merge bias: tasks with >2 predecessors get 5% duration increase per extra predecessor.

Run Monte Carlo simulation for probabilistic schedule risk analysis.

Samples task durations from triangular distributions and returns P10/P50/P80/P95 completion estimates with identified risk events. Use seed for reproducible results.

Data-driven estimate using reference class forecasting.

Applies historical correction factors based on actual-vs-estimated ratios. When no historical data exists, uses industry averages (1.3-2.2x for software tasks). Prioritize this over algorithmic models when historical data is available.

Recalculate team-specific correction factors from historical estimation data.

Compares estimated vs actual hours to compute a correction multiplier. Requires PM system integration for best results. Returns recommendations for improving estimation accuracy.

Map LLM token budgets to estimated wall-clock time.

Uses model-specific calibration data (tokens/second, reasoning overhead, tool-call latency) to estimate how long a task will actually take. Bridges the gap between token-space (how agents reason) and time-space (what humans need).

Estimate wall-clock time AND dollar cost for LLM token usage.

Combines token-to-time mapping with model-specific pricing data. Returns cost breakdown (input/output/overhead) alongside the time estimate.

Compare all LLM models side-by-side for a given token budget.

Ranks models by estimated cost or time. Shows quality tier for each model. Use when choosing which model to use for a task.

Track estimation accuracy improvement over time.

Computes sliding-window MAPE and compares against industry baseline (25%). Shows whether your estimates are improving, degrading, or stable. Industry research shows estimation accuracy does NOT improve with experience (Cao 2022) — self-correcting systems like Epoch can buck this trend.

Assess schedule risk for an estimate using historical accuracy data.

Computes confidence intervals (p50/p80/p95) based on your team's MAPE. Returns risk level and actionable recommendations. Uses industry baseline (25% MAPE) when no historical data is available.

Validate COCOMO estimation model against 195 real historical projects.

Runs the COCOMO Basic formula against projects from NASA93, COCOMO81, Albrecht, and Kemerer datasets. Reports overall MAPE, bias, per-type accuracy, and recommended coefficient adjustments.

Validate all COCOMO estimation models against 240 real historical projects with known effort.

Runs 6 models in parallel: COCOMO Basic, COCOMO II Nominal, COCOMO II + AI 12x speedup, and AI + developer profile at human/hybrid/ai_native gradients. Reports MAPE, MMRE, PRED(25), PRED(50), bias per model, with breakdowns by dataset and project type.

Submit actual hours for a previous estimate to improve future accuracy.

Pairs with any estimation tool. The estimate_id comes from the estimate response. Actuals feed into the self-improvement loop — after enough samples, correction factors update automatically to reduce estimation bias.

List recent estimates that have not yet received actual-hour feedback.

Returns estimates awaiting actuals so you can submit feedback via record_actual. Use this to close the estimation feedback loop and improve accuracy over time.

Record actual hours for multiple estimates in a single call.

Efficient for bulk feedback submission — accepts 1 to 500 entries at once. Each entry pairs an estimate ID with the actual hours spent.

Get a health report on the estimation feedback loop.

Shows total estimates, actuals, match rate, MAPE by tool and task type, and self-improvement readiness (which types have enough data for auto-calibration).