jev-mcp

An MCP server that gives any agent (Claude Code, Codex, Cursor, Pi, …) fast, typed, calibrated decisions from Jev, TypeSafe AI's System One model — classify, score, check yes/no, and gate risky tool calls, all as one API call under the hood.

🧪 Try Jev free in your browser (no waitlist): jevtypesafeai.com Independent playground & guide. Not affiliated with TypeSafe AI.

Jev doesn't write text — it reads your state and returns a choice, a score, or a probability in 70–500 ms, for ~$0.0004 a decision. That makes it a great "fuzzy if" inside the agent loop: routing, guardrails, scoring, filtering. jev-mcp exposes that as plain MCP tools your agent can call.

Tools

Related MCP server: jev-code

Install

You need a Jev API key. Two ways to get one:

• Hosted (instant, no waitlist): buy prepaid credit at jevtypesafeai.com/pricing and you get a jv_live_… key. No JEV_BASE_URL needed — the server detects a jv_live_ key and routes it to the hosted gateway automatically.

• Official TypeSafe: get a key at console.typesafe.ai. It's used against api.typesafe.ai by default.

⚠️ A hosted jv_live_… key is only valid at the jevtypesafeai.com gateway, not at api.typesafe.ai. Older versions of this server defaulted every key to the official endpoint, so a hosted key returned 401 authentication_error. That's now automatic — just set your key.

Runs straight from GitHub with npx — no clone, no build.

Claude Code

claude mcp add jev -e TYPESAFE_API_KEY=your_key -- npx -y github:codaaiteam/jev-mcp

Any MCP client (.mcp.json / config)

{
  "mcpServers": {
    "jev": {
      "command": "npx",
      "args": ["-y", "github:codaaiteam/jev-mcp"],
      "env": { "TYPESAFE_API_KEY": "your_key" }
    }
  }
}

That's it — your agent now has jev_classify, jev_score, jev_check, jev_gate, jev_decide. Works with a hosted jv_live_… key or an official TypeSafe key; the endpoint is chosen for you.

Once this is on npm you can shorten github:codaaiteam/jev-mcp to just jev-mcp.

Configuration

Examples

Guardrail a shell command before running it:

jev_gate({
  action: "rm -rf ./dist && aws s3 sync ./build s3://prod-assets --delete",
  context: "agent is deploying a frontend build"
})
→ { "recommendation": "confirm", "risk_score": 2.8, "touches_prod": true, ... }

Route a request to the right model:

jev_classify({
  state: "Refactor auth to multi-tenant SSO with SAML + SCIM, keep back-compat.",
  instructions: "Which model tier should handle this?",
  options: { fast: "trivial edits", balanced: "normal work", strong: "hard architecture" }
})
→ { "choice": "strong", "confidence": 0.99, "probabilities": { ... } }

Check before auto-approving user content:

jev_check({ state: "<user comment>", instructions: "Is this safe to auto-publish?" })
→ { "probability": 0.12, "likely": false }

How it works

Every tool is a thin wrapper over one Jev call — state + typed questions → typed answers. The value is in where you call it (the agent-loop hook) and what you ask. Because the answer type is fixed by the request, Jev can't hallucinate a format or emit an invalid type.

Links

• ▶️ Free playground & guide: https://jevtypesafeai.com

• 📖 What is Jev: https://jevtypesafeai.com/what-is-jev

• 🔌 API guide: https://jevtypesafeai.com/how-to-use

• 🌐 Ecosystem & gateways: https://jevtypesafeai.com/ecosystem

• 🏢 Official: https://typesafe.ai · Docs: https://docs.typesafe.ai

• 📚 More projects: awesome-jev

License

MIT. "Jev", "System One" and "TypeSafe AI" belong to their respective owners.