AI Guardrails

Provides security guardrails for AI interactions, including input validation, output filtering, policy enforcement, and audit logging.

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4 months ago

First Indexed

2 months ago

Catalog Refreshed

Documentation & install

Readme and setup notes from the catalogue, plus a client-ready config you can copy for your MCP host.

Installation

Add the following to your MCP client configuration file.

Configuration

View docs

{
  "mcpServers": {
    "expertvagabond-guardrails-mcp-server": {
      "command": "node",
      "args": [
        "/path/to/guardrails-mcp-server/index.js"
      ],
      "env": {
        "NODE_ENV": "production"
      }
    }
  }
}

You run an MCP server that applies guardrails to AI interactions, including input validation, output filtering, policy enforcement, and audit logging. This server helps you keep AI agents secure, compliant, and auditable while controlling usage and data exposure.

How to use

You connect the Guardrails MCP Server to your MCP client following standard MCP integration patterns. You will send incoming requests through the engine for validation, filtering, and policy checks, and you will process outputs to redact sensitive information before returning results. The server provides an API surface for processing inputs and outputs, getting statistics, and querying audit logs. Use it to enforce security policies across your AI deployments, maintain an audit trail, and apply rate limiting to prevent abuse.

How to install

Prerequisites: you need Node.js installed on your system. You will also use npm to install and run the server. Follow these steps to install and start the Guardrails MCP Server locally.

# Step 1: navigate to your working directory
cd ~/guardrails-mcp-server

# Step 2: install dependencies
npm install

# Step 3: run the server locally (examples shown assume entry at index.js)
npm run start

Additional setup details

The server architecture coordinates multiple security components to guard AI interactions. The Guardrails Engine orchestrates input validation, policy enforcement, and output filtering, with an audit logger recording requests, responses, and policy events. You can tune rate limiting and enable or disable components via configuration.

Available tools

processInput

Validates and processes incoming requests, applying input validation, rate limiting, and policy checks.

processOutput

Filters outgoing responses to redact sensitive information and apply output policies.

getStats

Retrieves current engine statistics such as usage and performance metrics.

getAuditLogs

Queries audit logs with optional filtering to support compliance and investigations.