Typesafe MCP

Give your AI agent typed judgments instead of free text. jev is an MCP server that lets Claude Code, Claude Desktop, and Codex call TypeSafe's Jev model and get back probabilities they can branch on. One command, jev mcp setup, registers it with all three.

┌──────────────┐  evaluate   ┌─────────┐  POST /v1/systemone  ┌──────────────┐
│ Claude Code  │ ──────────▶ │   jev   │ ───────────────────▶ │ TypeSafe API │
│ Claude Desk. │   (stdio)   │  (MCP)  │  retries 429 / 529   │  ── or ──    │
│ Codex        │ ◀────────── │         │ ◀─────────────────── │  OpenRouter  │
└──────────────┘ typed JSON  └─────────┘   POST /decisions    └──────────────┘

Why this exists

Problem: When an agent needs a yes/no call, a routing decision, or a severity rating, it usually asks an LLM, then parses prose and hopes the format holds. The answer has no probability attached, so the agent cannot tell a confident "yes" from a coin flip.

Solution: jev exposes one tool, evaluate, that sends state plus typed questions to Jev and returns structured answers with probabilities. Nothing to parse and no prompt formatting to maintain. jev mcp setup wires it into Claude Desktop, Claude Code, and Codex in one step.

Quickstart

1. Install (macOS and Linux, amd64 and arm64):

curl -fsSL https://raw.githubusercontent.com/itsmostafa/typesafe-mcp/main/install.sh | sh

It installs to ~/.local/bin. If that is not on your PATH, add it with export PATH="$HOME/.local/bin:$PATH". With Go, you can instead run go install github.com/itsmostafa/typesafe-mcp/cmd/jev@latest. Run jev update to upgrade in place.

2. Register with your agents (get a key at https://console.typesafe.ai/)

TYPESAFE_API_KEY=your-key jev mcp setup

Already on OpenRouter? Use that key instead and jev routes through OpenRouter's Decisions endpoint, billed to your OpenRouter account:

OPENROUTER_API_KEY=your-key jev mcp setup

TYPESAFE_API_KEY wins if both are set. OpenRouter's Decisions endpoint is still on its /api/alpha/ path and may move.

3. Ask your agent a judgment question

"Use jev to decide whether this ticket is urgent and which team should own it: Help! My payouts have been failing for 3 days."

The agent calls evaluate with:

{
  "state": "Help! My payouts have been failing for 3 days.",
  "questions": {
    "is_urgent": {"type": "noul", "instructions": "Does this convey urgency?"},
    "department": {"type": "choice", "instructions": "Which team should handle this?",
      "criteria": {"billing": "Payments, refunds", "technical": "Bugs, outages", "sales": "Pricing"}}
  }
}

It gets back the raw response JSON, with each answer under the same id you gave it.

What you get

• One-command setup across clients. jev mcp setup registers with Claude Code (user scope) and Codex when their CLIs are on PATH, and with Claude Desktop when it is installed. Every TYPESAFE_* variable in your shell is carried over, plus OPENROUTER_API_KEY. Re-run it to update.
• Answers your code can branch on. Three question types: noul (probability a condition holds), choice (one option from a map), score (position on ordered levels).
• Rate limits handled for you. 429 and 529 responses are retried with exponential backoff. Other API errors come back to the agent as tool errors it can read and act on.
• Several questions, one call. Batch independent questions over the same state; they run in parallel.
• Agents that use it well out of the box. The server ships usage guidance (narrow questions, JSON state, no-match options) to the client, so the agent writes better questions without extra prompting.
• A single static binary. No runtime, no Node, no Python. jev update upgrades it in place from a checksum-verified release. Read-only tool, 60s request timeout, response size capped at 16 MiB.

About TypeSafe

TypeSafe builds System One models: small units of AI intelligence you use like programming primitives. Instead of generating text, they turn natural language and application state into typed judgments and probabilities that code can combine. Jev is one of them.

Website · Docs · API reference · Console

Reference

evaluate

Criteria by type: noul takes optional {"true": ..., "false": ...} descriptions; choice requires a map of option to description; score requires an ordered array of at least 2 levels. Full docs: https://docs.typesafe.ai/api

Manual client config

Skip jev mcp setup and point your client at /absolute/path/to/jev mcp with TYPESAFE_API_KEY (or OPENROUTER_API_KEY) in its env. Restart Claude Desktop after any config change.

Contributing

Issues and pull requests are welcome. The repo uses Task:

task check     # gofmt, go vet, and tests with -race
task inspect   # open the MCP Inspector against a local build

If jev saves you some prompt-parsing, a star helps others find it.