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Ai Rete Rag MCP Server

AI & MLUse Caution4.8MCP RegistryLocalRemote
Free

Server data from the Official MCP Registry

Author rules from policy docs, then decide: a Rete engine gives the verdict, an LLM explains why.

About

Author rules from policy docs, then decide: a Rete engine gives the verdict, an LLM explains why.

Remote endpoints: streamable-http: https://ai-rete-rag.com/mcp streamable-http: https://ai-rete-rag.com/mcp/auth

Security Report

4.8
Use Caution4.8High Risk

The ai·rete·rag MCP server demonstrates strong security practices with proper authentication, credential isolation, and access controls. The code implements context-aware credential handling via ContextVars to prevent cross-request leakage in the shared HTTP endpoint, and enforces strict CORS/DNS-rebinding protections. Minor concerns around input validation and error handling do not substantially undermine the security posture. Supply chain analysis found 5 known vulnerabilities in dependencies (0 critical, 5 high severity). Package verification found 1 issue.

5 files analyzed · 11 issues found

Security scores are indicators to help you make informed decisions, not guarantees. Always review permissions before connecting any MCP server.

Permissions Required

This plugin requests these system permissions. Most are normal for its category.

HTTP Network Access

Connects to external APIs or services over the internet.

env_vars

Check that this permission is expected for this type of plugin.

File System Read

Reads files on your machine. Normal for tools that analyze or process local data.

What You'll Need

Set these up before or after installing:

Your ai·rete·rag API key (create one at ai-rete-rag.com under Settings -> API Keys). Authenticates calls and ties them to your plan quota.Required

Environment variable: AI_RETE_RAG_API_KEY

API base URL. Defaults to https://ai-rete-rag.com; override for local development.Optional

Environment variable: AI_RETE_RAG_API_URL

How to Install & Connect

Available as Local & Remote

This plugin can run on your machine or connect to a hosted endpoint. during install.

Documentation

View on GitHub

From the project's GitHub README.

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

ToolWhat it does
decideMake a decision in a domain — structured facts and/or free text, with optional Pattern 01 (rules scope retrieval) and Pattern 02 (retrieval into working memory)
list_rulesInspect a domain's rules — conditions, verdicts, salience, overlaps
get_rule_sourceFetch a domain's rule set as editable YAML
import_policy_rulesTurn a written policy document into draft rules, each citing the sentence it encodes (nothing is saved — review, then put_rules)
put_rulesCreate or replace a domain's rule set from YAML (dry_run to validate)
ingest_textAdd policy text to a domain's knowledge base
list_documentsBrowse a domain's ingested documents
get_usageCheck your plan and remaining monthly decision quota

Connect by URL (no install)

The server is also hosted, which is the only route for clients that connect to a URL and have no field for a static header — claude.ai and Claude Desktop custom connectors, in particular.

URLAuthReaches
https://ai-rete-rag.com/mcp/authSign in with Google, once, in the browserYour account — your domains, your plan quota
https://ai-rete-rag.com/mcpNone, or Authorization: Bearer ik_...Shared demo domains anonymously; your account with a key

Add https://ai-rete-rag.com/mcp/auth as a custom connector and approve the prompt. Connections are listed under Settings → Connected apps, and disconnecting one takes effect immediately.

Install

No install needed with uvuvx 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.)

Or skip the install entirely and point it at the hosted endpoint with your key:

claude mcp add --transport http ai-rete-rag https://ai-rete-rag.com/mcp \
  --header "Authorization: Bearer ik_your-key-here"

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:

VariableDefaultPurpose
AI_RETE_RAG_API_KEY(none)Your API key — authenticates calls and ties them to your plan quota
AI_RETE_RAG_API_URLhttps://ai-rete-rag.comAPI base URL — point at http://localhost:8000 for local dev

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.

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Ai Rete Rag MCP Server - Author rules from policy docs, then decide: a Rete engine | MCP Marketplace