simulate-monte-carlo
Real Monte Carlo simulation of a compound event or conditional probability. Named random variables, a safe expression interpreter, seedable for reproducibility. Priced per call via x402/USDC on Base.
README
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), aneventboolean expression over those names (e.g."a > 0.5 && b == 1"), and an optionalconditionexpression to get a conditional probabilityP(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 — 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
npm install
2. Run the unit tests
npm test
3a. If your wrangler dev works (macOS 13.5+, Linux, Windows)
npm run dev
3b. If wrangler dev fails because of the macOS version
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
# 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, Smithery, mcp.so, and with an open PR to 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, changeNETWORKinsrc/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.
Recommended Servers
playwright-mcp
A Model Context Protocol server that enables LLMs to interact with web pages through structured accessibility snapshots without requiring vision models or screenshots.
Magic Component Platform (MCP)
An AI-powered tool that generates modern UI components from natural language descriptions, integrating with popular IDEs to streamline UI development workflow.
Audiense Insights MCP Server
Enables interaction with Audiense Insights accounts via the Model Context Protocol, facilitating the extraction and analysis of marketing insights and audience data including demographics, behavior, and influencer engagement.
VeyraX MCP
Single MCP tool to connect all your favorite tools: Gmail, Calendar and 40 more.
graphlit-mcp-server
The Model Context Protocol (MCP) Server enables integration between MCP clients and the Graphlit service. Ingest anything from Slack to Gmail to podcast feeds, in addition to web crawling, into a Graphlit project - and then retrieve relevant contents from the MCP client.
Kagi MCP Server
An MCP server that integrates Kagi search capabilities with Claude AI, enabling Claude to perform real-time web searches when answering questions that require up-to-date information.
E2B
Using MCP to run code via e2b.
Neon Database
MCP server for interacting with Neon Management API and databases
Exa Search
A Model Context Protocol (MCP) server lets AI assistants like Claude use the Exa AI Search API for web searches. This setup allows AI models to get real-time web information in a safe and controlled way.
Qdrant Server
This repository is an example of how to create a MCP server for Qdrant, a vector search engine.