simulate-monte-carlo
Real Monte Carlo simulation of a compound event/conditional probability. Paid via x402.
Open source Repository Open in the app JSON README (API)
About
Real Monte Carlo simulation of a compound event/conditional probability. Paid via x402.
Details
- Kind
- MCP servers
- Topic
- Finance & crypto
- Publisher
- encodi
- Origin
- official
- Category
- ferramentas
- Transport
- http
- Version
- 1.0.0
- Stars
- 1
- Last push
- 2026-08-11T17:29:42Z
- Repository state
- ativo
- Language
- TypeScript
- License
- MIT
- Added
- 2026-08-29 03:02:45
- Updated
- 2026-08-29 03:02:45
- Origin id
io.github.encodi/simulate-monte-carlo
README
# simulate-monte-carlo
[](https://glama.ai/mcp/servers/encodi/simulate-monte-carlo)
Remote MCP server (Cloudflare Workers) with one tool that estimates a compound event or conditional probability by actually running a Monte Carlo simulation:
- **`simulate_monte_carlo`** — declare named random variables (`uniform`, `normal`, `bernoulli`, `binomial`, `poisson`, `exponential`, `discrete`), an `event` boolean expression over those names (e.g. `"a > 0.5 && b == 1"`), and an optional `condition` expression to get a conditional probability `P(event | condition)` via rejection sampling. Real random sampling and real counting — not a model guess about what the probability should be.
No database, no persistent state: each call builds a fresh `McpServer` (see `createServer()` in `src/index.ts`) and is self-contained. The PRNG is seedable (`mulberry32`): pass the `seed` returned in a previous response to reproduce the exact same result.
## Why a hand-written expression interpreter, not `eval`
`event`/`condition` are arbitrary caller-supplied strings. Running them through `eval`/`Function` would mean executing untrusted code inside the Worker. Instead, `src/tools/monteCarlo.ts` includes a small tokenizer + recursive-descent parser + AST interpreter that only understands numbers, declared variable names, arithmetic (`+ - * /`), comparisons (`< <= > >= == !=`), boolean logic (`&& || !`), parentheses, and a three-function whitelist (`min`, `max`, `abs`). There is no code execution path — the interpreter can't do anything beyond evaluate that narrow grammar.
## Billing (x402)
Charges per call via [x402](https://x402.org) — real USDC payment on **Base mainnet**, against the Coinbase Developer Platform (CDP) facilitator. The payment travels inside the MCP JSON-RPC itself (`_meta`), not as an HTTP header; see `src/payments.ts`.
| Tool | Price |
|---|---|
| `simulate_monte_carlo` | $0.03 USDC |
An unpaid `tools/call` returns `isError: true` with the `accepts` (network, amount, `payTo`) the client needs to pay and retry — not an unexplained exception.
## Structure
```
src/
index.ts # registers the tool in the McpServer and exposes the MCP HTTP handler
payments.ts # x402 billing on Base mainnet via the CDP facilitator
tools/
monteCarlo.ts # distributions, expression parser/interpreter, simulation loop (testable without Workers)
monteCarlo.test.ts
scripts/
dev-node.ts # dev server that runs the handler in plain Node, no wrangler
```
## Resource limits
Set from the start, not bolted on after: max 10 variables, 100–100,000 trials (default 10,000), 500-character expressions, binomial `n` ≤ 1,000, poisson `lambda` ≤ 1,000, and a discrete-outcome cap of 20. On top of the individual caps, a combined sampling-budget check (`trials × sum(per-variable cost)` ≤ 5,000,000) rejects combinations that would be individually within limits but jointly too expensive — e.g. 100,000 trials against a `binomial(n=1000)` variable.
## Running it locally
⚠️ **Note on `wrangler dev`**: the real Cloudflare Workers runtime (`workerd`) requires **macOS 13.5+**. If your Mac has an older version, `wrangler dev` (and `npm run dev`) will fail. This project includes a plain-Node shim that runs the exact same `fetch()` handler without needing `workerd`.
### 1. Install dependencies
```bash
npm install
```
### 2. Run the unit tests
```bash
npm test
```
### 3a. If your wrangler dev works (macOS 13.5+, Linux, Windows)
```bash
npm run dev
```
### 3b. If `wrangler dev` fails because of the macOS version
```bash
npm run dev:node
```
Starts at `http://localhost:8787/mcp`, reading CDP credentials from `~/.mcp-tools-factory-credentials.env` (shared across all tools in this factory).
### 4. Test with curl
```bash
# 1) initialize
curl -s -X POST http://localhost:8787/mcp \
-H "Content-Type: application/json" \
-H "Accept: application/json, text/event-stream" \
-d '{"jsonrpc":"2.0","id":1,"method":"initialize","params":{"protocolVersion":"2025-06-18","capabilities":{},"clientInfo":{"name":"curl-test","version":"0.0.1"}}}'
# 2) tools/list
curl -s -X POST http://localhost:8787/mcp \
-H "Content-Type: application/json" \
-H "Accept: application/json, text/event-stream" \
-d '{"jsonrpc":"2.0","id":2,"method":"tools/list","params":{}}'
# 3) tools/call — simulate_monte_carlo (two dice, P(sum > 9 | first die == 6))
curl -s -X POST http://localhost:8787/mcp \
-H "Content-Type: application/json" \
-H "Accept: application/json, text/event-stream" \
-d '{"jsonrpc":"2.0","id":3,"method":"tools/call","params":{"name":"simulate_monte_carlo","arguments":{"variables":[{"name":"d1","distribution":{"type":"discrete","values":[1,2,3,4,5,6],"weights":[1,1,1,1,1,1]}},{"name":"d2","distribution":{"type":"discrete","values":[1,2,3,4,5,6],"weights":[1,1,1,1,1,1]}}],"event":"d1 + d2 > 9","condition":"d1 == 6","trials":40000,"seed":3}}}'
```
Responses come as Server-Sent Events (`event: message` + `data: {...}`); the `data:` line is the usual JSON-RPC response.
## Deploy and listings
Deployed at `https://simulate-monte-carlo.encodari.workers.dev/mcp` (Cloudflare Workers). Published on the [official MCP registry](https://registry.modelcontextprotocol.io), [Smithery](https://smithery.ai), [mcp.so](https://mcp.so), and with an open PR to [awesome-mcp-servers](https://github.com/punkpeye/awesome-mcp-servers).
## What it doesn't do (yet)
- Charges on Base mainnet with real money. To switch back to testnet (Base Sepolia, `eip155:84532`) during development, change `NETWORK` in `src/payments.ts`.
- No database or persistent state between calls (beyond the billing config, cached in memory per isolate — see `src/payments.ts`).
- The 95% confidence interval uses the normal (Wald) approximation, which is imprecise near probabilities close to 0 or 1 — good enough for a quick estimate, not a substitute for exact binomial confidence intervals in high-stakes use.