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KyaniteLabs/Epoch

KyaniteLabs/Epoch

Software estimation: PERT, COCOMO II, Monte Car
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Config template (adjust for your client)
claude mcp add epoch -- npx -y epoch
A pasteable skeleton generated from the repository name. Check the project README for the exact package and arguments.
Star count
8 ★ · KyaniteLabs/Epoch · read from the GitHub search API · (checked 2026-10-16 20:00:20)
Source
collected from the punkpeye/awesome-mcp-servers list · type: MCP server
Harvest history
first seen 2026-10-09 · last seen 2026-10-16 · seen 32× · listed by 7 sources
Score
score 703 · 8 ★ · 7 sources · mcp
FAQ (answers come from our records)
How do I install it?
We did not find an install command for this repository (our install coverage is only about 4%). Check its README or releases page.
Who is it for?
We filed it under these tags: MCP 服务. The tags come from the entry itself, not a taxonomy we imposed.
Who maintains it?
repository KyaniteLabs/Epoch;first catalogued 2026-10-09;last seen 2026-10-16。There is no official certification or quality rating here — we record sources and dates only.
Third-party quality signals

Epoch -- Time Estimation MCP Server

以下摘录自该仓库的 README(片段) · 在 GitHub 读完整 README →

> Epoch is a time estimation MCP server that helps engineering leads, agents, and planners who need calibrated duration estimates produce PERT/reference-class estimates and record actuals.
**TL;DR:** Epoch — time estimation MCP server. Best for engineering leads, agents, and planners who need calibrated duration estimates.
**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
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 26 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: task-type correction factors and scope baselines from the maintainer's recorded estimate-vs-actual history plus curated public research, and tool-usage/latency telemetry samples (117,791 across the bundled canary-test and session benchmarks) that keep the token-time bridge grounded. You get reasonable 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:**
That's it. Your AI assistant now has 26 time estimation tools.
Or add it to your project's .mcp.json:
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 16 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 26 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, context classification, calibration, token-time bridge | reference_class_estimate, estimate_from_context, 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, cocomo_ground_truth |
| **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
**convert_timezone** -- Convert a timestamp between IANA timezones
Source
awesome · https://github.com/KyaniteLabs/Epoch
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