@puenteworks/epoch

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.

Accuracy note: Reference class baselines are built from 39 real AI-native tasks across 4 repositories. With correct complexity calibration, estimates fall within 25% of actuals ~67% of the time (vs ~25% for unaided human experts per Jorgensen 2004). Accuracy improves as teams submit estimated-vs-actual feedback through the self-improvement engine.

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 @puenteworks/epoch

That's it. Your AI assistant now has 21 time estimation tools.

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

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

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

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

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

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: 0,
  baselineSource: "real_tasks_28",
  confidence: "pessimistic",
  developerProfile: { mode: "ai_native", estimationMape: 15, underestimationBias: 0.2, correctionFactor: 1.45 },
  adjustedEstimate: 9.7,
  note: "Using reference database correction factors. Submit actuals via /v1/feedback/record-actual to improve accuracy."
}

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) — 3 samples, need 10.",
    "Submit actuals via POST /v1/feedback/record-actual to enable data-driven calibration."
  ]
}

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,
    completionTokens: 35000,
    toolOverheadSeconds: 2
  }
}

Layer 5 -- Cost & Risk

token_cost_estimate -- Token cost estimation for LLM API calls

Input:  {
  tokens: 50000,
  model: "claude-sonnet-4-6"
}
Output: {
  tokens: 50000,
  model: "claude-sonnet-4-6",
  estimatedSeconds: 36,
  estimatedMinutes: 0.6,
  estimatedCost: 0.57,
  costBreakdown: { inputCost: 0.045, outputCost: 0.525, toolCallOverheadCost: 0 },
  confidence: "likely"
}

compare_models -- Side-by-side cost and capability comparison across LLM models

Input:  {
  tokens: 50000,
  sort_by: "cost"
}
Output: {
  tokens: 50000,
  models: [
    { model: "gemini-2.0-flash", estimatedCost: 0.0155, qualityTier: "fast", tokensPerSecond: 230 },
    { model: "deepseek-v3", estimatedCost: 0.0189, qualityTier: "fast", tokensPerSecond: 150 },
    { model: "claude-sonnet-4-6", estimatedCost: 0.57, qualityTier: "fast", tokensPerSecond: 140 }
  ],
  sortBy: "cost"
}

accuracy_trend -- Track estimation accuracy over time from recorded feedback data

Input:  { team_id: "backend", window_size: 50 }
Output: {
  overallTrend: "improving",
  currentMape: 26.5,
  industryBaselineMape: 25,
  totalEstimates: 1049,
  totalWithActuals: 1049,
  windows: [{ period: "Window 1 (estimates 1-50)", mape: 32, bias: 5.3, sampleSize: 50 }]
}

schedule_risk -- Schedule risk scoring for project timelines

Input:  {
  estimated_hours: 40,
  task_type: "feature"
}
Output: {
  estimatedHours: 40,
  riskLevel: "low",
  confidenceIntervals: { p50: 40, p80: 45.1, p95: 49.9 },
  historicalAccuracy: { mape: 15, sampleSize: 0 },
  recommendation: "Low risk. Estimate is within normal variance.",
  humanReadable: "Schedule risk: low. MAPE: 15% (based on 0 historical records). Confidence intervals: p50=40h, p80=45.1h, p95=49.9h."
}

cocomo_validate -- Validate COCOMO II estimates against reference data

Input:  {}
Output: {
  projectsEvaluated: 182,
  mape: 85.55,
  bias: 53.5,
  byProjectType: {
    organic: { mape: 86.57, count: 22 },
    semidetached: { mape: 84.75, count: 106 },
    embedded: { mape: 86.71, count: 54 }
  },
  recommendedAdjustments: []
}

ai_native Mode

Epoch tools support dual estimation modes to account for the fundamentally different velocity of AI-assisted vs human-only development.

When ai_native=true (default), tools use Epoch's reference database with tool-aware correction factors. These baselines reflect AI agent workflows: faster iteration, higher output volume, and different error profiles.

When ai_native=false, tools apply human developer baselines:

Parameter	Human Baseline	AI-Native Baseline
Feature development	14 calendar days (industry data)	5.7h median (28 real tasks)
Bug fix turnaround	72 hours (industry data)	6.2h median (8 real tasks)
Sprint velocity	35 story points (industry data)	80 story points
Estimation accuracy (MAPE)	25% (Jorgensen 2004)	15% (from AI-native profiles)
Correction factor	1.8x (industry standard)	1.07-1.45x (from reference DB)

Tools that support ai_native: pert_estimate, cocomo_estimate, sprint_forecast, reference_class_estimate, schedule_risk.

Hybrid workflows: ai_native accepts a float from 0.0 (fully human) to 1.0 (fully AI-native). Values like 0.5 produce interpolated profiles for mixed AI/human workflows. Boolean values (true/false) remain supported for backward compatibility.

Self-Improvement Engine

Epoch gets better the more you use it. The self-improvement engine works through a feedback loop:

• Estimate -- Generate an initial estimate with any estimation tool
• Record -- Track the actual outcome (time, cost, effort)
• Calibrate -- calibrate_estimates computes correction factors from your estimated vs actual data
• Improve -- Future estimates automatically apply updated correction factors
• Trend -- accuracy_trend tracks whether your estimation accuracy is improving over time

Estimated vs Actual -> Correction Factor -> Better Estimates -> Repeat

The engine detects systematic biases (chronic under-estimation, accuracy degradation) and surfaces actionable recommendations.

Community Data

Help improve Epoch by contributing anonymized estimation data. Community contributions expand the reference database, improve baseline accuracy for all users, and help calibrate AI-native vs human estimation modes.

See CONTRIBUTING-data.md for guidelines on data format, privacy requirements, and submission process.

Surfaces

Epoch exposes the same 24 tools through three interfaces:

Surface	Transport	Use Case
MCP Server	stdio	Claude Code, Cursor, VS Code, Windsurf
CLI	Direct invocation	Scripts, CI/CD, quick lookups
REST API	HTTP (Hono)	Web apps, AI agents, integrations

Default behavior: running epoch with no arguments starts the MCP stdio server.

CLI

# PERT estimate
epoch pert-estimate --optimistic 2 --most-likely 4 --pessimistic 12 --unit hours

# Token-to-time bridge
epoch token-time-bridge --tokens 50000 --model claude-sonnet-4-20250514

# Monte Carlo simulation
epoch monte-carlo-schedule --tasks '[{"name":"A","optimistic":2,"most_likely":4,"pessimistic":8}]'

# COCOMO II estimate
epoch cocomo-estimate --kloc 15 --project-type organic

# Schedule risk score
epoch schedule-risk --tasks '[{"name":"A","duration":5,"risk_level":"high"},{"name":"B","duration":3,"risk_level":"low"}]'

# List all tools
epoch list-tools

# Pretty table output
epoch pert-estimate --optimistic 2 --most-likely 4 --pessimistic 12 --pretty

REST API

# Start the server
epoch serve --port 3099
# or: EPOCH_TRANSPORT=http EPOCH_PORT=3099 epoch

# Call any tool
curl -X POST http://localhost:3099/v1/tools/pert_estimate \
  -H "Content-Type: application/json" \
  -d '{"optimistic": 2, "most_likely": 4, "pessimistic": 12, "unit": "hours"}'

# Health check
curl http://localhost:3099/health

# OpenAPI spec
curl http://localhost:3099/openapi.json

For AI Agents

Epoch provides built-in discoverability endpoints so AI agents can find and use the API without prior configuration:

Endpoint	Description
GET /.well-known/ai-plugin.json	OpenAI plugin manifest
GET /llms.txt	LLM-consumable documentation
GET /openapi.json	OpenAPI 3.1 specification
GET /health	Service health and version

Installation

git clone https://github.com/KyaniteLabs/Epoch.git
cd Epoch
pnpm install
pnpm run build

Development

pnpm test          # Run test suite (896 tests)
pnpm run build     # Build with tsup
pnpm run typecheck # TypeScript strict mode check
pnpm run dev       # Run development server
pnpm run inspector # Open MCP Inspector for interactive testing

Tech Stack

• Runtime: Node.js 20+ (ESM)
• Language: TypeScript 5.8 (strict mode, noUncheckedIndexedAccess, verbatimModuleSyntax)
• Validation: Zod 3.24 with .describe() on every field
• MCP SDK: @modelcontextprotocol/sdk 1.12+
• HTTP: Hono (lightweight, multi-runtime)
• CLI: Commander.js
• Date Handling: date-fns 4.x + date-fns-tz 3.x
• Build: tsup (ESM output)
• Testing: vitest 3.x with v8 coverage (97% statements, 88% branches)

Configuration

Variable	Default	Description
EPOCH_TRANSPORT	stdio	Transport mode: stdio or http
EPOCH_PORT	3000	HTTP server port
EPOCH_HOST	127.0.0.1	HTTP server bind address
EPOCH_DATA_DIR	~/.epoch/	Data directory for feedback and self-improvement
EPOCH_COMMUNITY_DIR	data/community/	Community data directory
EPOCH_RATE_LIMIT	100	Max requests per minute per IP (HTTP only)

License

MIT License. See LICENSE for full terms.