Epoch -- Time Estimation MCP Server

Epoch helps AI agents understand time.

AI can write code, analyze data, and build apps -- but it can't tell you how long something will take, what it'll cost, or whether a deadline is realistic. Epoch fixes that.

Show Me

You: "How long will a 15,000 line project take with a small team?"

Claude (using Epoch):
  COCOMO II nominal: 100 person-months (traditional team)
  AI-adjusted: 9 person-months (LLM-assisted workflow)
  Schedule risk: medium (MAPE 15%, based on 28 historical features)
  Cost estimate (Claude Sonnet): ~$340 in API tokens

You: "How many working days between now and launch?"

Claude (using Epoch):
  Start: May 1, 2026
  End: July 15, 2026
  Business days: 54
  Weekend days: 16
  Holidays: 1 (Independence Day)
  Total calendar days: 75

You: "Which AI model should we use for a 50k-token job?"

Claude (using Epoch):
  gemini-2.0-flash:  $0.02 -- fast, budget-friendly
  gpt-4o-mini:       $0.02 -- fast, budget-friendly
  gemini-2.5-pro:    $0.37 -- standard, high quality
  claude-sonnet-4:    $0.57 -- standard, high quality
  Recommendation: gemini-2.0-flash for cost, claude-sonnet-4 for quality

Why Epoch?

Every AI agent hallucinates timelines. "This should take about 2 hours" becomes 2 days. Epoch gives AI grounded, data-driven estimates instead of guesses. It packages established estimation methods (PERT, COCOMO II, Monte Carlo, reference class forecasting) into 24 tools any AI can call -- so your assistant stops guessing and starts calculating.

Works out of the box. Epoch ships with a bundled reference database built from 126,223 real data points across task types, complexity levels, and estimation tools. You get accurate estimates from day one — no data collection or account setup required. If you choose to record your actuals, Epoch's self-improvement engine learns your patterns and gets even more precise over time.

What is MCP?

MCP (Model Context Protocol) is how AI assistants like Claude connect to external tools. Think of it like a plugin system -- you add Epoch with one command, and suddenly your AI assistant can estimate timelines, calculate business days, compare model costs, and predict whether your project will finish on time.

Quick Start

30-second setup -- works in Claude Code, Cursor, VS Code, and Windsurf:

claude mcp add epoch -- npx @kyanitelabs/epoch

That's it. Your AI assistant now has 24 time estimation tools.

Or add it to your project's .mcp.json:

{
  "mcpServers": {
    "epoch": {
      "command": "npx",
      "args": ["@kyanitelabs/epoch"]
    }
  }
}

Agent Skill

Epoch also ships a public agent skill at skills/epoch/SKILL.md. Use $epoch in compatible agent hosts when you want the agent to choose the right Epoch MCP or CLI workflow for time estimates, business-day math, model-cost comparison, schedule risk, and estimate-vs-actual feedback.

What Can Epoch Do?

What you want	What Epoch does	No jargon
"How long will this take?"	Gives you a realistic estimate with best/worst case ranges	Estimates
"Can we hit this deadline?"	Tells you if your timeline is realistic or risky	Schedule risk
"How much will the AI calls cost?"	Calculates token costs across 12 AI models side-by-side	Cost comparison
"How many business days between now and launch?"	Counts days excluding weekends and holidays (5 countries)	Calendar math
"Are our estimates getting better?"	Tracks your accuracy over time and auto-corrects	Self-improving
"What model should we use?"	Compares speed, cost, and quality across all major AI models	Model comparison

Technical Reference

Everything below is for developers who want to understand the internals, use the CLI or REST API, or contribute to Epoch.

Architecture

Six-layer design with 24 tools for time estimation, scheduling, cost analysis, and feedback:

Layer	Purpose	Tools
1. Core Temporal	Time, timezones, duration, date math	`get_current_time`, `convert_timezone`, `parse_duration`, `time_math`
2. Calendar Math	Business days, holidays (US/UK/FR/DE/JP)	`add_business_days`, `count_business_days`
3. Estimation	PERT, COCOMO II, sprint, CPM, Monte Carlo	`pert_estimate`, `cocomo_estimate`, `sprint_forecast`, `critical_path`, `monte_carlo_schedule`
4. Analytics	Reference class, calibration, token-time bridge	`reference_class_estimate`, `calibrate_estimates`, `token_time_bridge`
5. Cost & Risk	Token cost, model comparison, accuracy trends, risk, COCOMO validation	`token_cost_estimate`, `compare_models`, `accuracy_trend`, `schedule_risk`, `cocomo_validate`
6. Feedback	Record actuals, track pending estimates, batch operations, health checks	`record_actual`, `get_pending_estimates`, `batch_record_actuals`, `feedback_health`

Tool Reference

Layer 1 -- Core Temporal

get_current_time -- Current wall-clock time in any IANA timezone

Input:  { timezone: "America/New_York" }
Output: {
  iso: "2026-05-01T08:30:00.000-04:00",
  humanReadable: "Fri, May 1, 2026, 8:30 AM EDT",
  timezone: "America/New_York",
  utcOffset: "-04:00"
}

convert_timezone -- Convert a timestamp between IANA timezones

Input:  { timestamp: "2026-05-01T12:00:00Z", target_tz: "Asia/Tokyo" }
Output: {
  iso: "2026-05-01T21:00:00.000+09:00",
  timezone: "Asia/Tokyo",
  utcOffset: "+09:00",
  humanReadable: "Fri, May 1, 2026, 9:00 PM JST"
}

parse_duration -- Parse human-readable duration strings

Input:  { duration_string: "2h30m" }
Output: {
  input: "2h30m",
  totalSeconds: 9000,
  humanReadable: "2 hours 30 minutes"
}

time_math -- Date arithmetic operations

Input:  { operation: "add_days", date: "2026-05-01", value: 7 }
Output: {
  result: "2026-05-08T00:00:00.000Z",
  operation: "add_days",
  input: "2026-05-01"
}

Supported operations: add_days, add_business_days, diff, convert_tz, parse_nl, format_duration

Layer 2 -- Calendar Math

add_business_days -- Add N business days with holiday awareness (US, UK, FR, DE, JP)

Input:  { start_date: "2026-05-01", days: 5, country: "US" }
Output: {
  startDate: "2026-05-01",
  endDate: "2026-05-08",
  businessDays: 5,
  countryCode: "US",
  humanReadable: "5 business days from 2026-05-01 to 2026-05-08 (US)."
}

count_business_days -- Count business days between two dates

Input:  { start_date: "2026-05-01", end_date: "2026-05-15", country: "US" }
Output: {
  startDate: "2026-05-01",
  endDate: "2026-05-15",
  businessDays: 10,
  countryCode: "US",
  humanReadable: "10 business days between 2026-05-01 and 2026-05-15 (US)."
}

Layer 3 -- Estimation

pert_estimate -- PERT three-point estimation with confidence intervals and urgency scoring

Input:  {
  optimistic: 2,
  most_likely: 4,
  pessimistic: 12,
  unit: "hours"
}
Output: {
  expected: 5,
  variance: 2.78,
  stdDeviation: 1.67,
  confidence95: [1.67, 8.33],
  confidence99: [0, 10],
  unit: "hours",
  urgencyCategory: "medium",
  humanReadable: "Expected: 5 hours. 95% confidence: 1.67 to 8.33 hours. 99% confidence: 0 to 10 hours.",
  developerProfile: { mode: "ai_native", correctionFactor: 1.45 },
  adjustedEstimate: 7.25
}

cocomo_estimate -- COCOMO II software sizing with LLM-adapted cost drivers

Input:  {
  kloc: 15,
  reasoning_complexity: 1.2,
  context_completeness: 1.0,
  transformation_impact: 0.8,
  iterative_cycles: 1.5,
  human_oversight: 1.2
}
Output: {
  kloc: 15,
  personMonthsNominal: 99.9,
  personMonthsLlmAdjusted: 8.9,
  effortMultipliers: {
    reasoning_complexity: 1.2,
    context_completeness: 1.0,
    transformation_impact: 0.8,
    iterative_cycles: 1.5,
    human_oversight: 1.2,
    product: 1.728
  },
  developerProfile: { mode: "ai_native", correctionFactor: 1.45 }
}

LLM-adapted cost drivers include reasoning complexity, context completeness, transformation impact, iterative cycles, and human oversight requirements.

sprint_forecast -- Sprint velocity forecasting from historical data

Input:  {
  backlog_points: 100,
  velocity_history: [20, 25, 22, 23],
  sprint_length_days: 14,
  hours_per_sprint: 80
}
Output: {
  backlogPoints: 100,
  averageVelocity: 22.5,
  requiredSprints: 4.4,
  pessimisticSprints: 4.9,
  hoursPerPoint: 3.56,
  totalHours: 355.6,
  completionDays: 62,
  sprintLengthDays: 14,
  developerProfile: { mode: "ai_native", sprintVelocityPoints: 80, correctionFactor: 1.45 }
}

critical_path -- Critical Path Method with merge-bias adjustment for parallel tasks

Input:  {
  tasks: [
    { name: "A", duration: 5, predecessors: [] },
    { name: "B", duration: 3, predecessors: ["A"] },
    { name: "C", duration: 4, predecessors: ["A"] }
  ]
}
Output: {
  critical_path: ["A", "C"],
  total_duration: 9,
  slack_per_task: { A: 0, B: 1, C: 0 },
  merge_bias_adjustment: 0
}

monte_carlo_schedule -- Monte Carlo simulation with seeded PRNG for deterministic, reproducible results

Input:  {
  tasks: [
    { name: "A", optimistic: 2, most_likely: 4, pessimistic: 8 },
    { name: "B", optimistic: 1, most_likely: 3, pessimistic: 6 }
  ],
  iterations: 10000
}
Output: {
  p10: "5.9",
  p50: "7.91",
  p80: "9.39",
  p95: "10.75",
  riskEvents: [{ description: "Task \"A\" exceeded 1.5x PERT expected in 5% of simulations", probability: 0.05, impactDays: 3 }],
  criticalPathProbability: 0.8
}

Layer 4 -- Analytics

reference_class_estimate -- Reference class forecasting with planning fallacy correction

Input:  {
  task_type: "feature",
  complexity: 3
}
Output: {
  rawEstimate: 6.7,
  correctedEstimate: 11.1,
  correctionFactor: 1.67,
  sampleSize: 126223,
  baselineSource: "self-improvement",
  confidence: "pessimistic",
  developerProfile: { mode: "ai_native", estimationMape: 15, underestimationBias: 0.2, correctionFactor: 1.45 },
  adjustedEstimate: 9.7,
  note: "Correction factors from bundled reference database (126,223 samples). Record actuals to personalize further."
}

Valid task_type values: feature, bugfix, refactor, migration, infrastructure, documentation, testing, design.

calibrate_estimates -- Team-specific accuracy calibration from historical estimated vs actual data

Input:  {
  task_type: "feature",
  team_id: "backend"
}
Output: {
  correctionFactor: 1.45,
  accuracyTrend: "stable",
  velocityTrend: "stable",
  recommendations: [
    "Using reference database correction factor (1.45x) — personalized from 126,223 samples.",
    "Record actuals via POST /v1/feedback/record-actual to refine for your team's patterns."
  ]
}

token_time_bridge -- Map LLM token budgets to wall-clock time for 12 model families

Input:  {
  tokens: 50000,
  model: "claude-sonnet-4-20250514",
  tool_calls: 10,
  reasoning_depth: "deep"
}
Output: {
  estimatedSeconds: 697,
  estimatedMinutes: 11.6,
  confidence: "likely",
  urgency: "short",
  breakdown: {
    promptTokens: 15000,
   

…

Epoch

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