# 📚 Nexus guide: guardrails

*Published 2025-12-05*

## What are guardrails?

Guardrails are security policies that control how AI assistants interact with tools and data. Guardrails provide nuanced rules that make AI systems safer and more predictable.

## Why guardrails matter

When AI assistants use tools, they need different constraints than human users:

- Content limits: An AI shouldn’t read 10GB files that would overwhelm its context
- Semantic filtering: Block prompts trying to access sensitive data patterns
- Behavioral rules: Prevent actions that make sense for humans but not for AI
- Dynamic policies: Adjust permissions based on the conversation context

## Types of guardrails

| Input | Output | Behavioral |
| --- | --- | --- |
| **Control what goes into the AI system:**

• Domain filtering: Limit which websites can be accessed\
• File type restrictions: Block binary files or specific formats\
• Size limits: Cap file sizes and directory traversal depth\
• Pattern matching: Detect and block sensitive data patterns | **Control what comes out of tools:**

• Data sanitization: Remove sensitive information from responses\
• Format enforcement: Ensure outputs match expected schemas\
• Content filtering: Block inappropriate or harmful content\
• Response limits: Prevent overwhelming the AI with data | **Control how the AI uses tools:**

• Rate limiting: Prevent excessive API calls\
• Sequence controls: Enforce proper tool usage order\
• State validation: Ensure operations happen in valid states\
• Audit requirements: Force logging of certain operations |

## How to add safety constraints to tool operations

1\. Identify the MCP server and tool. First, you need to know:

- The MCP server ID (the server providing the tool)
- The specific tool/operation name you want to constrain

2\. List available guardrail templates. This shows you all the constraint templates available for that specific tool, with descriptions of what each does:

3\. Add a guardrail, once you know which template to use:

4\. To manage existing guardrails:

- List guardrails: See all constraints on a specific tool or server
- Remove guardrails: Remove constraints by their ID

## Templates to enforce data validation rules

Guardrail templates provide pre-configured validation rules that you can apply to tool parameters and responses. Here are the common types:

Value constraints:

- min/max values: Enforce numeric ranges (e.g., budget must be ≤ $10,000)
- min/max length: Control string/array sizes (e.g., descriptions must be ≤ 500 chars)
- enum/allowed values: Whitelist specific values (e.g., status must be "draft", "active", or "paused")
- blocked values: Blacklist certain values

Pattern validation:

- regex patterns: Match specific formats (e.g., email addresses, phone numbers)
- data type validation: Ensure correct types (string, number, boolean, etc.)

Structural validation:

- required fields: Ensure certain parameters are always provided
- schema path targeting: Apply rules to nested data structures using JSONPath

## Scope guardrails to specific servers or tools

Guardrails use a hierarchical scoping system that lets you target constraints at different levels of specificity.

1\. **Server-level scope** allows you to apply guardrails to an entire MCP server. It affects all tools within that server:

> Tip: When you omit `operationName`, the guardrail applies globally to all operations from that server.

2\. **Tool-level scope** *(most common)* allows you to apply guardrails to a specific tool/operation within a server. This targets just one tool, giving you fine-grained control:

3\. **Parameter-level scope** allows you apply guardrails more precisely. Within a tool, you can target specific parameters or parts of a data structure using `schemaPath`. The JSONPath you put in `schemaPath` lets you drill down to the exact field you want to constrain:

## List existing guardrails

1\. To list all guardrails for a server:

2\. To list guardrails for a specific tool:

The list will show you:

- Constraint ID (you'll need this to remove it)
- Template type being used
- The constraint value
- Schema path (if targeting specific parameters)
- When it was added

## Remove guardrails

## Toolkit data hooks

1\. Pre-populate tool parameters automatically (`Hook_Parameter`)

Automatically set parameter values so the AI doesn't need to ask for them.

Use case: Always use a specific account ID, region, or configuration value.

2\. Filter which tools are visible to the AI (`Hook_Filter`)

Hide tools from the AI to reduce noise or prevent access to certain operations.

Use case: Only show read operations, hide dangerous delete functions.

3\. Create aliases or rename tools (`Hook_ Alias`)

Give tools friendlier or more descriptive names.

Use case: Rename technical tool names to business-friendly terms.

4\. Clone tools with different configurations (`Hook_Clone`)

Duplicate a tool with a new name, allowing different parameter presets.

Use case: Create "production" and "staging" versions of the same tool.

5\. Override or add to tool descriptions (`Hook_CustomDescription`)

Change how tools are described to the AI for better context.

Use case: Add business context, warnings, or usage guidelines.

## Managing hooks

1\. Read existing hooks:

2\. Describe hook structure and examples:

3\. Delete hooks:

Source: /news/guardrails