@rvry/mcp

Reasoning Depth Enforcement for LLMs.

RVRY is an MCP server that improves AI reliability by forcing your model to finish thinking before it answers. When your AI raises a question, RVRY remembers it. When it makes an assumption, RVRY flags it. When it tries to wrap up while those are still unaddressed, RVRY doesn't let it.

Quick Start

npx @rvry/mcp setup

The wizard will:

1. Open your browser to sign in (or prompt for a token)
2. Auto-detect supported clients on your machine
3. Configure them automatically

Supported clients: Claude Code, Claude Desktop, Cursor, Gemini CLI, Codex, Anti-Gravity

Restart any running clients after setup, and RVRY is ready.

Options

npx @rvry/mcp setup --token rvry_abc123    # Skip browser auth, use token directly
npx @rvry/mcp setup --client code           # Only configure Claude Code
npx @rvry/mcp setup --client desktop        # Only configure Claude Desktop
npx @rvry/mcp setup --accept-terms          # Accept ToS non-interactively

Manual Installation

Claude Code:

claude mcp add -e RVRY_TOKEN=rvry_your_token -s user rvry -- npx @rvry/mcp

Claude Desktop (claude_desktop_config.json):

{
  "mcpServers": {
    "RVRY": {
      "command": "npx",
      "args": ["@rvry/mcp"],
      "env": {
        "RVRY_TOKEN": "rvry_your_token_here"
      }
    }
  }
}

Config file locations:

• macOS: ~/Library/Application Support/Claude/claude_desktop_config.json
• Windows: %APPDATA%\Claude\claude_desktop_config.json
• Linux: ~/.config/Claude/claude_desktop_config.json

Other MCP clients:

Any client that supports MCP can use RVRY. Point it at npx @rvry/mcp with the environment variable RVRY_TOKEN set to your token.

Tools

• RVRY_deepthink — Extended analysis that catches assumptions, tests them, and doesn't let your AI wrap up until it's dealt with what it found.
• RVRY_problem_solve — Structured decision-making that forces your AI through orientation, anticipation, and evaluation before it commits to a recommendation.

How it Works

Same model. Same question. Different answer.

Your AI already has the reasoning capacity. RVRY forces it to use that capacity by holding it accountable — when it raises a question, that question has to get answered. When it makes an assumption, that assumption has to get tested. It keeps working not because it was told to think harder, but because it hasn't finished what it started.

On hard questions, a smaller model with RVRY outperforms a bigger model without it. The bottleneck was never how smart the AI is. It was whether the AI finished thinking.

Learn more →

Pricing

| Plan | Runs | Price | |------|------|-------| | Free | 5/month | $0 | | Pro | 25/month | $20/mo | | Max | Unlimited | $100/mo |

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