ai·rete·rag MCP Server

Use ai·rete·rag — deterministic rule-based decisions with RAG-powered explanations — from Claude Code, Claude Desktop, or any MCP client.

The verdict always comes from the Rete rule engine (auditable, reproducible); the LLM only explains why, grounded in your ingested policy documents.

Tools

Related MCP server: OOSDK MCP Server

Install

No install needed with uv — uvx ai-rete-rag-mcp fetches and runs the server on demand (see the config snippets below).

Alternatively, install it as a package:

pip install ai-rete-rag-mcp         # from PyPI
pip install .                       # or from source, in this repo

Configure

First create an API key: sign in at ai-rete-rag.com, open Settings → API Keys, and create a key (ik_... — shown once).

Claude Code

claude mcp add ai-rete-rag -e AI_RETE_RAG_API_KEY=ik_your-key-here -- uvx ai-rete-rag-mcp

(If you installed via pip, use -- ai-rete-rag-mcp instead of -- uvx ai-rete-rag-mcp.)

Claude Desktop / other clients (JSON)

{
  "mcpServers": {
    "ai-rete-rag": {
      "command": "uvx",
      "args": ["ai-rete-rag-mcp"],
      "env": {
        "AI_RETE_RAG_API_KEY": "ik_your-key-here"
      }
    }
  }
}

(With a pip install, set "command": "ai-rete-rag-mcp" and drop "args".)

Environment variables:

Without a key you can still explore the shared demo domains (loan, fraud, clinical, …) subject to free-tier limits.

Example

"Use ai·rete·rag to decide whether this loan application should be approved: credit score 645, annual income $52k, requested amount $30k."

Claude calls decide(domain="loan", facts={...}) and returns the rule-derived verdict plus a plain-English explanation citing the underwriting policy.