About gamenumerics
gamenumerics provides deterministic numerical engines for game designers and small teams working on balance, spreadsheet-based design, and live-ops audits. Its stdio MCP server exposes 17 read-only tools for importing spreadsheets, analyzing formulas, and running simulations without LLM-generated numeric guesses.
Key features
- 01Import and normalize XLSX tables
- 02Simulate battles and gacha outcomes
- 03Analyze growth curves and game formulas
- 04Infer column patterns and table relations
- 05Grade and profile workspaces
- 06Read-only MCP tool surface
Use cases
- 01Audit progression and growth balance
- 02Simulate battle or gacha outcomes
- 03Infer formulas from spreadsheet tables
MCP support
The stdio MCP server exposes 17 read-only tools to MCP clients, including spreadsheet import and table inspection, numerical analysis and simulation, and workspace operations.
Configuration
Local server config is the same for every client. Copy it into your client’s MCP config file.
npx -y mcp-server-gamenumericsGND_WORKSPACES_DIRWorkspace root directory where imported xlsx tables land (default: ~/.gamenumerics/workspaces)Local servers need a runtime on your machine (e.g. Node.js 18+). Config file locations differ by client; see your client’s docs.
Tools
17Saved tool list · updated 2w ago
| Parameter | Type | Required | Description |
|---|---|---|---|
| module | string | No | Filter by module (optional): settings/growth/equipment/level/hero/economy/rogue/talent/enemy/gamedata |
Details
From the project READMEgamenumerics
Deterministic numeric engines for game design — exposed to your AI coding agent via MCP. Import xlsx config tables, audit growth curves, run battle/gacha simulations, and reverse-engineer the formulas behind game spreadsheets. Every numeric answer comes from zero-dependency pure-function engines — zero LLM guessing.
Who it's for: game designers and solo/small teams doing roguelike / deckbuilder balance work, spreadsheet-driven numerical design, or live-ops tuning audits — no engine integration required, just an xlsx export of your config tables.
Quick start (MCP server)
The fastest way to use these engines is through mcp-server-gamenumerics — a stdio MCP server exposing 17 read-only tools to Claude Code, Cursor, ZCode, or any MCP host:
npx -y mcp-server-gamenumerics
{ "mcpServers": { "gamenumerics": { "command": "npx", "args": ["-y", "mcp-server-gamenumerics"] } } }
Full per-host config (Claude Code / Cursor / ZCode), the 17-tool reference, and the performance baseline live in mcp-server/README.md.
What's inside
| Path | What it is |
|---|---|
lib/engine/formula-engine/ | 19 pure-function numeric engines: battle simulation, gacha probability + Monte Carlo, EHP×EDPS power model, growth projection, economy production/consumption, progression curves, level systems, difficulty mirroring |
lib/table/ | xlsx/spreadsheet normalization: dual-row header merge, column pattern detection (arithmetic/geometric), base×coefficient relation inference, Theil-Sen robust curve fitting |
lib/agent/tools/ | Tool definitions (JSON Schema + handlers) wrapping the engines — the read/audit surface |
mcp-server/ | The npm-distributed stdio MCP server (mcp-server-gamenumerics, esbuild single-file bundle) |
scripts/lib/workspace-import.ts | xlsx → normalized JSON workspace import pipeline |
Tool surface (17 = 14 mapped + 3 meta)
import_xlsx · list_tables / read_table · battle_simulate / simulate_gacha / compute_power / power_curve / eval_formula / audit_column / infer_column_rule / infer_table_relation · grade_workspace / profile_table / infer_foreign_keys · list_workspaces / set_workspace / read_memory
Read-only by design — write tools stay behind the web workbench's human-in-the-loop confirm flow (separate product, not part of this repo).
Development
npm install
npm test # vitest: engine + table + mcp-server suites
npm run check # tsc --noEmit (lib + scripts)
npm run mcp:check # tsc for mcp-server (own tsconfig)
npm run mcp:build # esbuild bundle → mcp-server/dist/index.js
License
Comments
New to MCP?
From what the protocol is to installing your first server, these answers cover most first questions.
An open protocol that defines how AI applications connect to external tools and data in a uniform way. Clients talk to the model; MCP servers provide tools, resources and prompt templates. They exchange standard messages, so one server can be reused by many clients.