SMB Sandbox
Mock small-business CRM, inbox, calendar, invoicing and phone tools for testing AI agents safely.
Runs on your machine from its package; the steps are below.
What it does
- Reads
- Writes
- High impact
Not assessed yet.
What it costs
FreeOdel charges nothing per call. The publisher may charge for its own service.
Runs locally over stdio
This server isn't hosted — your MCP client launches it from a package registry. Use one of the commands below, or drop the config into your client (e.g. Claude Desktop).
Run
npx -y @teamshift/sandbox-mcp
MCP client config
{
"mcpServers": {
"smb-sandbox": {
"command": "npx",
"args": [
"-y",
"@teamshift/sandbox-mcp"
]
}
}
}smb-sandbox is an open-source, fully fictional small business for testing AI agents: a realistic dataset generator, mock CRM, inbox, calendar, invoicing and phone systems exposed as MCP servers, and verified business tasks graded on the end state of the business, not on what the agent says it did.
Agents that look great in a demo often fall apart on a real company's messy data: a duplicate contact, a quote nobody followed up on, a payment that doesn't match any invoice. You can't safely test against real customers, and random fake rows don't have those problems. smb-sandbox gives you a believable company that does, with every problem labeled so you can measure whether your agent fixed it.
What's inside
| Package | What it does | Start here |
|---|---|---|
@teamshift/fake-business | Generates a coherent fictional business (customers, leads, deals, quotes, jobs, invoices, payments, emails, SMS, calls, tasks and an event timeline) with 11 kinds of labeled operational mess. Deterministic, zero dependencies. Exports JSON, CSV, SQL, HubSpot- and QuickBooks-shaped CSVs. | npx @teamshift/fake-business --industry dental-clinic --summary |
@teamshift/sandbox-mcp | 46 MCP tools over that business (CRM, inbox, calendar, invoicing, phone). Writes are validated, audited, and never leave the process. Sending email only writes to an outbox. | claude mcp add sandbox -- npx -y @teamshift/sandbox-mcp |
@teamshift/smb-workflows | 10 real small-business tasks (quote follow-up, missed-call callbacks, invoice reminders, duplicate cleanup and more), each with a deterministic verifier and a reference solution. | npx @teamshift/smb-workflows list |
Three starter industries: home services (HVAC and plumbing), dental clinic, and marketing agency. Each seed is a different company with the same kinds of problems.
Evaluate an agent in three commands
# 1. Start the sandbox business and record everything the agent does
npx @teamshift/sandbox-mcp --industry home-services --seed 42 --toolsets crm,inbox,calendar,invoicing,phone --state-out run.json
# 2. Give your agent the task prompt (Claude Code, Cursor, or your own agent connected to the server above)
npx @teamshift/smb-workflows show quote-follow-up --industry home-services --seed 42
# 3. Grade the end state
npx @teamshift/smb-workflows grade quote-follow-up --industry home-services --seed 42 --state run.json
The grade checks what actually changed: the right records were updated, the right people got exactly one message each, nothing outside the task was touched, and no money moved unless the task allowed it. See docs/run-with-claude-code.md for a full walkthrough.
Use the data on its own
npx @teamshift/fake-business --industry marketing-agency --seed 7 --format csv --out ./agency
npx @teamshift/fake-business --industry home-services --format sql-sqlite --out business.sql
import { generate } from "@teamshift/fake-business";
const biz = generate({ industry: "dental-clinic", seed: 7 });
const overdue = biz.invoices.filter((i) => i.status === "open" && i.dueOn < biz.meta.asOf);
console.log(biz.company.name, overdue.length, "overdue invoices");
console.log(biz.anomalies.map((a) => a.kind)); // ground truth for grading
FAQ
How do I test an AI agent without real customer data?
Generate a fictional company with fake-business, connect your agent to it through sandbox-mcp, and grade the result with smb-workflows. Every name, email, phone number and address is invented (.example domains, 555-01xx numbers), so you can run tests, record demos and publish results without exposing anyone.
Is this a benchmark?
It's the material for one. The 10 workflows are a starting set of verified tasks with deterministic graders, so you can compare agents, models or prompts on the same company. We don't publish a leaderboard yet.
Why grade the end state instead of the agent's answer?
Agents regularly report success they didn't achieve. A verifier that diffs the records, the audit log and the outbox against ground truth catches hallucinated completions, duplicate emails, and changes to records the task never mentioned.
Does it work with Claude, Cursor or my own agent?
Yes. sandbox-mcp is a standard Model Context Protocol server over stdio (or Streamable HTTP), so any MCP client works. The sandbox-mcp README has config snippets for Claude Code, Claude Desktop, Cursor and VS Code.
Can I add an industry or a task?
Yes, and it's the most useful contribution. An industry is one file; a task is a prompt, a verifier and a reference solution. See CONTRIBUTING.md.
Development
corepack enable
pnpm install
pnpm build && pnpm test && pnpm typecheck
Citing
If you use smb-sandbox in research, please cite it using CITATION.cff.
License
Apache-2.0. All generated data is fictional and free to use, modify and publish.
Built by TeamShift — AI workers for small-business operations.
Three-year library
The generator now exposes 100 stable three-year company configurations, a trailer-dealer archetype, opt-in source-bound staffing, invariant checks, an illustrative double-entry ledger and trailer-stock movements. See the generator guide and dataset card. Build locally, then export with node scripts/export-library.mjs <output-directory>. The dataset is published on Hugging Face and Zenodo.