Jev MCP Proxy

A high-performance Model Context Protocol (MCP) proxy server that routes System One evaluation requests from AI agent skills directly to TypeSafe AI's Jev model.

Background: System One & Jev

As introduced by TypeSafe AI in Introducing System One Models & Jev, System One models represent a new class of frontier AI designed for automation within code. Rather than generating conversational strings token-by-token, Jev takes program state and answers typed questions in parallel with calibrated probabilities.

• Fast & Hardware-Aware: 70ms–500ms end-to-end response time.

• Typed Outputs: Strictly evaluates against defined rubrics (no string hallucinations or schema mismatches).

• Calibrated Uncertainty: Communicates exact confidence scores and probabilities.

This MCP server acts as the dedicated router between agent skills (in Google Antigravity, Claude Code, Cursor, etc.) and Jev.

┌─────────────────────────────────────────────────────────────┐
│                       Agent Platform                        │
│             (Google Antigravity / Claude Code)             │
│                                                             │
│   ┌──────────────────┐             ┌────────────────────┐   │
│   │  Routing Skill   │             │  Moderation Skill  │   │
│   └────────┬─────────┘             └─────────┬──────────┘   │
└────────────┼─────────────────────────────────┼──────────────┘
             │           MCP Tools             │
             ▼                                 ▼
┌─────────────────────────────────────────────────────────────┐
│                    Jev MCP Proxy Server                     │
│    (jev_evaluate, jev_choice, jev_noul, jev_score)          │
│                                                             │
│  - JSON-RPC stdio transport                                 │
│  - Auto-retries with exponential backoff on 429 & 529       │
│  - Stderr request tracing & latency measurement             │
└──────────────────────────────┬──────────────────────────────┘
                               │ HTTPS / Bearer Token
                               ▼
┌─────────────────────────────────────────────────────────────┐
│                 TypeSafe AI Jev API                         │
│         (https://api.typesafe.ai/v1/systemone)              │
└─────────────────────────────────────────────────────────────┘

Related MCP server: mewcp-jev

Features

• Lean & Focused: The only functionality of this MCP server is to route requests to Jev when skills need them.

• Full Parallel Evaluation: jev_evaluate allows evaluating arbitrary state against multiple typed questions in a single round-trip.

• Convenience Tool Shortcuts: High-level tools (jev_choice, jev_noul, jev_score) for common skill operations.

• Resilience: Automatic retry logic with exponential backoff for HTTP 429 (Rate Limit) and 529 (Service Overloaded).

• Live Tracing: Emits structured timing, token usage, and status traces directly to stderr (visible in MCP host logs without interfering with JSON-RPC over stdout).

Exposed MCP Tools

1. jev_evaluate

Primary batch router for TypeSafe AI Jev. Evaluates state (text or structured data) against multiple typed questions simultaneously.

Parameters:

• state (string | object | array, required): The application state, document, log, or conversation context to evaluate.

• questions (object, required): Dictionary of question IDs mapping to question definitions.

  • type: "noul" | "choice" | "score"

  • instructions: Prompt or criteria instructions.

  • criteria: Required for choice (map of options to descriptions) and score (list of level strings). Optional for noul.

• model (string, optional): Jev model version (default: "jev-latest").

• api_key (string, optional): Override for TYPESAFE_API_KEY.

Example:

{
  "state": "The user reported: Database pool connection timeout after 30s in checkout service.",
  "questions": {
    "is_incident": {
      "type": "noul",
      "instructions": "Does this indicate a production incident?"
    },
    "service_area": {
      "type": "choice",
      "instructions": "Which domain team owns this?",
      "criteria": {
        "database": "Database servers and connection pooling",
        "billing": "Checkout, credit cards, payment logic",
        "frontend": "UI buttons and rendering"
      }
    }
  }
}

2. jev_choice

Categorical classification shortcut. Selects one option from a defined set, returning the selected choice, confidence score, and full probability distribution.

Parameters:

• state (string | object | array): Context to evaluate.

• instructions (string | object | array): What the model should decide.

• criteria (object): Dictionary mapping option keys to descriptive rubrics.

• model (string, optional): Default "jev-latest".

• question_id (string, optional): Default "choice".

Example Response:

{
  "choice": "database",
  "confidence": 0.88,
  "probabilities": {
    "database": 0.88,
    "billing": 0.10,
    "frontend": 0.02
  },
  "usage": { "input_tokens": 140, "output_tokens": 25 }
}

3. jev_noul

Binary probability shortcut. Evaluates a yes/no question and returns the calibrated probability ($0.0 \le P \le 1.0$) that the statement is true.

Parameters:

• state (string | object | array): Context to evaluate.

• instructions (string | object | array): The yes/no question.

• criteria (object, optional): Optional definitions for {"true": "...", "false": "..."}.

Example Response:

{
  "probability": 0.95,
  "is_likely": true,
  "usage": { "input_tokens": 98, "output_tokens": 12 }
}

4. jev_score

Rubric rating shortcut. Rates state across an ordered rubric of at least two descriptive levels, returning a weighted score and confidence.

Parameters:

• state (string | object | array): Context to evaluate.

• instructions (string | object | array): Rating criteria.

• criteria (array of strings): Ordered list of descriptive levels (e.g. ["Low", "Medium", "High", "Critical"]).

Example Response:

{
  "score": 2.8,
  "confidence": 0.84,
  "legend": { "0": "Low", "1": "Medium", "2": "High", "3": "Critical" },
  "probabilities": { "0": 0.0, "1": 0.05, "2": 0.15, "3": 0.80 }
}

Installation & Setup

Prerequisites

• Python 3.10+

• uv (recommended) or pip

git clone https://github.com/altregubov/jev-antigravity-mcp.git
cd jev-antigravity-mcp
uv sync

Environment Variables

• TYPESAFE_API_KEY: Your API key from the TypeSafe AI Console.

• TYPESAFE_BASE_URL: (Optional) Custom API endpoint (default: https://api.typesafe.ai/v1/systemone).

• JEV_DEBUG: Set to "1" (default) to log request traces to stderr.

Configuration

Google Antigravity 2.0

Option A: Zero-Install from GitHub via uvx (Recommended)

No local repository cloning needed. Antigravity will automatically fetch and run the server in an isolated environment.

Add the following to your global Antigravity MCP configuration (~/.gemini/config/mcp_config.json):

{
  "mcpServers": {
    "jev-proxy": {
      "command": "uvx",
      "args": [
        "--from",
        "git+https://github.com/altregubov/jev-antigravity-mcp",
        "jev-mcp"
      ],
      "env": {
        "TYPESAFE_API_KEY": "YOUR_TYPESAFE_API_KEY"
      }
    }
  }
}

Option B: Via the Antigravity 2.0 Desktop UI

1. Open the Antigravity 2.0 desktop app.

2. In the left-hand sidebar, click Skills & Customizations (or click the ... menu in the top-right $\rightarrow$ MCP Servers).

3. Click Add MCP Server (or +).

4. Configure as Stdio Transport:

   • Name: jev-proxy

   • Command: uvx

   • Arguments: --from git+https://github.com/altregubov/jev-antigravity-mcp jev-mcp

   • Environment Variables:

     • TYPESAFE_API_KEY: YOUR_TYPESAFE_API_KEY

5. Click Save & Connect.

Option C: Local Development / Cloned Repository

If you cloned the repository locally:

{
  "mcpServers": {
    "jev-proxy": {
      "command": "uv",
      "args": [
        "run",
        "--directory",
        "/path/to/jev-antigravity-mcp",
        "jev-mcp"
      ],
      "env": {
        "TYPESAFE_API_KEY": "YOUR_TYPESAFE_API_KEY"
      }
    }
  }
}

Note: After adding the server, start a New Conversation or click Refresh in Skills & Customizations to mount the tools (jev_evaluate, jev_choice, jev_noul, jev_score).

Claude Code / Cursor

Add to your Claude Code or Cursor MCP configuration:

{
  "mcpServers": {
    "jev-proxy": {
      "command": "uv",
      "args": [
        "run",
        "--directory",
        "/path/to/jev-antigravity-mcp",
        "jev-mcp"
      ],
      "env": {
        "TYPESAFE_API_KEY": "YOUR_TYPESAFE_API_KEY"
      }
    }
  }
}

Tracing and Diagnostics

The proxy logs structured traces to stderr without interfering with the JSON-RPC communication on stdout:

[17:45:03] [jev_proxy] INFO: --> Routing request to JEV: POST https://api.typesafe.ai/v1/systemone | model=jev-latest | questions=['is_incident', 'service_area']
[17:45:04] [jev_proxy] INFO: <-- 200 OK from JEV in 184.2ms | model=jev-1.13.0 | tokens(in=320, out=45)

To see complete HTTP wire traces (request headers, connection pools, raw bytes):

HTTPX_LOG_LEVEL=trace uv run jev-mcp

Running Tests

The test suite covers client routing, question shortcuts, error handling (401, 422), rate limit backoff retries, and stdio JSON-RPC handshakes:

uv run pytest

License

MIT License. See LICENSE for details.