Ranger RAG MCP Server

MCP server that integrates Apache Ranger authorization with RAG (Retrieval Augmented Generation). When a user queries a knowledge base, the server first checks Ranger policies to verify the user has permission — if denied, the query is rejected before reaching the RAG system.

Architecture

User (AI Agent) ──→ MCP Server ──→ Ranger Policy Check ──→ RAG Studio
                                        │
                                  DENY → "Access Denied"
                                  ALLOW → Forward query, return results

Features

• Per-knowledge-base authorization — Ranger policies control which users can access which knowledge bases
• Transparent enforcement — denied queries never reach the RAG system
• Policy-based access control — uses existing Ranger infrastructure (policies, users, groups)
• Automatic retries — exponential backoff on transient errors
• Fallback evaluation — if Ranger's evaluateOnce API isn't available, evaluates policies locally

MCP Tools

Tool	Description
query_knowledge_base(user, knowledge_base, query)	Query a KB with Ranger auth check
list_knowledge_bases(user)	List KBs the user can access
check_access(user, knowledge_base, access_type)	Pre-flight permission check
list_policies()	Show all RAG Ranger policies (admin)

Setup

1. Create a Ranger Service for RAG

In Ranger Admin, create a new service (or use an existing custom service type) with:

• Service Name: rag
• Resource: knowledge_base (string, supports wildcards)
• Access Types: read, write

2. Create Ranger Policies

Example policies:

Policy Name	Resource	Users	Access
Finance KB - Analysts	Finance KB	alice, bob	read
HR KB - HR Team	HR Policies	charlie	read
All KBs - Admin	*	admin	read, write

3. Install and Configure

git clone <repo-url>
cd ranger-rag-mcp
python3 -m venv .venv
source .venv/bin/activate
pip install -e .

Copy .env.example to .env and fill in your values:

cp .env.example .env
# Edit .env with your Ranger and RAG Studio credentials

4. Configure MCP Client

Claude Desktop (~/Library/Application Support/Claude/claude_desktop_config.json):

{
  "mcpServers": {
    "ranger-rag-mcp-server": {
      "command": "/FULL/PATH/TO/ranger-rag-mcp/.venv/bin/python",
      "args": ["-m", "ranger_rag_mcp_server.server"],
      "env": {
        "RANGER_GATEWAY_URL": "https://<gateway>/<topology>/cdp-proxy-api/ranger/",
        "RANGER_USER": "<workload_username>",
        "RANGER_PASS": "<workload_password>",
        "RANGER_SERVICE_NAME": "rag",
        "RAG_STUDIO_URL": "https://<rag-studio-url>",
        "RAG_STUDIO_API_KEY": "<api_key>"
      }
    }
  }
}

Agent Studio / Kiro:

{
  "mcpServers": {
    "ranger-rag-mcp-server": {
      "command": "uvx",
      "args": [
        "--from",
        "git+https://github.com/<your-org>/ranger-rag-mcp@main",
        "run-server"
      ],
      "env": {
        "RANGER_GATEWAY_URL": "https://<gateway>/<topology>/cdp-proxy-api/ranger/",
        "RANGER_USER": "<workload_username>",
        "RANGER_PASS": "<workload_password>",
        "RANGER_SERVICE_NAME": "rag",
        "RAG_STUDIO_URL": "https://<rag-studio-url>",
        "RAG_STUDIO_API_KEY": "<api_key>"
      }
    }
  }
}

Configuration

Ranger

Variable	Required	Description
RANGER_GATEWAY_URL	Yes	Ranger Admin REST API URL via Knox
RANGER_USER	Yes	Workload username for Ranger API auth
RANGER_PASS	Yes	Workload password for Ranger API auth
RANGER_SERVICE_NAME	No	Ranger service name (default: rag)

RAG Studio

Variable	Required	Description
RAG_STUDIO_URL	Yes	RAG Studio base URL
RAG_STUDIO_API_KEY	Yes	RAG Studio API key
RAG_STUDIO_PROJECT_ID	No	Project ID (default: 1)
RAG_RESPONSE_CHUNKS	No	Number of chunks to retrieve (default: 5)
RAG_INFERENCE_MODEL	No	LLM model for response generation

TLS/HTTP

Variable	Default	Description
VERIFY_SSL	true	Set false to disable SSL verification
CA_BUNDLE	—	Path to CA certificate bundle
HTTP_TIMEOUT_SECONDS	30	Request timeout in seconds

Example Usage

Once configured, ask the AI:

# User with access → gets results
"As user 'alice', query the 'Finance KB' knowledge base: What was Q3 revenue?"

# User without access → gets denied
"As user 'bob', query the 'HR Policies' knowledge base: What is the PTO policy?"

# Check what a user can access
"List all knowledge bases that user 'alice' can access"

# Admin: see all policies
"Show me all the RAG access policies"

How It Works

1. User calls query_knowledge_base(user="alice", knowledge_base="Finance KB", query="...")
2. MCP server calls Ranger: POST /service/plugins/policies/evaluateOnce — "Can alice read Finance KB?"
3. Ranger evaluates policies:
   • Checks all enabled policies for the rag service
   • Looks for policies where resource knowledge_base matches "Finance KB"
   • Checks if user "alice" or any of her groups appear in policyItems with read access
4. If ALLOWED: Forward query to RAG Studio, return answer
5. If DENIED: Return ACCESS_DENIED with reason — RAG Studio is never contacted

Ranger Policy Structure

The server expects Ranger policies with this structure:

{
  "service": "rag",
  "name": "Finance KB Access",
  "isEnabled": true,
  "resources": {
    "knowledge_base": {
      "values": ["Finance KB"],
      "isRecursive": false
    }
  },
  "policyItems": [
    {
      "users": ["alice", "bob"],
      "groups": ["finance-team"],
      "accesses": [
        {"type": "read", "isAllowed": true}
      ]
    }
  ]
}

License

Apache License 2.0