The Best Way to Give CLI Tools to AI Agents

AI agents are becoming increasingly capable, but they’re often limited to what they can do with text alone. The real power comes when you give AI access to tools—especially command-line tools that developers already use daily.

But how do you actually connect CLI tools to AI in a way that’s secure, maintainable, and actually works? That’s what this guide is all about.

Why CLI Tools Are Perfect for AI Agents

Command-line tools have several properties that make them ideal for AI integration:

• Text-based I/O: CLI tools communicate through text, which LLMs naturally understand
• Composable: Small tools that do one thing well can be chained together
• Battle-tested: Most CLI tools have been refined over years of production use
• Well-documented: Man pages and help flags provide context AI can learn from

Think about what’s already available: git for version control, curl for HTTP requests, jq for JSON processing, gh for GitHub, docker for containers. These tools represent decades of collective engineering knowledge.

The question isn’t whether AI should use CLI tools—it’s how to make that happen safely and efficiently.

Three Approaches to Giving AI CLI Access

There are several ways to bridge the gap between AI models and command-line tools. Let’s examine each.

1. Raw Shell Access (Not Recommended)

The simplest approach is giving an AI agent direct shell access:

import subprocess
result = subprocess.run(command, shell=True, capture_output=True)

Problems with this approach:

• Security nightmare: Arbitrary command execution is dangerous
• No structure: AI has to parse unstructured text output
• No guardrails: Nothing prevents destructive operations
• Context overload: AI doesn’t know which tools are available or how to use them

This is like giving someone the keys to your house without telling them where anything is—or that the stove is on.

2. Custom Function Wrappers

A better approach is wrapping specific CLI tools as functions:

def search_github(query: str, language: str = None) -> dict:
    """Search GitHub repositories."""
    cmd = ["gh", "search", "repos", query, "--json", "name,url,stars"]
    if language:
        cmd.extend(["--language", language])
    result = subprocess.run(cmd, capture_output=True, text=True)
    return json.loads(result.stdout)

Then expose this as a tool to your AI:

tools = [
    {
        "name": "search_github",
        "description": "Search for GitHub repositories",
        "parameters": {
            "type": "object",
            "properties": {
                "query": {"type": "string", "description": "Search query"},
                "language": {"type": "string", "description": "Filter by language"}
            },
            "required": ["query"]
        }
    }
]

Benefits:

• Controlled, validated inputs
• Structured JSON output
• Clear documentation for the AI
• Security through restriction

Drawbacks:

• You have to write wrappers for every tool
• Maintenance burden as tools update
• Duplication across projects
• Discovery problem: AI only knows about tools you’ve wrapped

3. Model Context Protocol (MCP)

The Model Context Protocol is an open standard that solves the tool integration problem at the protocol level. Instead of writing custom wrappers, MCP provides a standard interface for tools.

An MCP server exposes tools that any MCP-compatible AI client can discover and use:

server.tool("github_search", {
  description: "Search GitHub repositories",
  inputSchema: {
    type: "object",
    properties: {
      query: { type: "string" },
      language: { type: "string" }
    },
    required: ["query"]
  },
  handler: async ({ query, language }) => {
    // Implementation
  }
});

Benefits of MCP:

• Standard protocol: One integration, many clients
• Tool discovery: AI can query available tools at runtime
• Typed schemas: Clear contracts between AI and tools
• Ecosystem: Growing library of pre-built MCP servers
• Security: Fine-grained permissions and sandboxing

MCP is quickly becoming the standard for AI tool integration, supported by Claude, Cursor, Windsurf, and other major AI platforms.

The Package Manager Approach

Here’s the thing: even with MCP, someone still needs to wrap CLI tools and publish them as servers. That’s a lot of work for each tool.

This is where a package manager for AI tools becomes valuable. Instead of building MCP servers from scratch, you install pre-built packages:

# Install GitHub tools
cli4ai add github

# Install Slack tools
cli4ai add slack

# Now AI can use both
cli4ai start github slack

Each package:

• Wraps CLI functionality with proper JSON schemas
• Handles authentication and secrets securely
• Provides structured output for AI consumption
• Works as both a CLI tool and MCP server

This is the approach we took with cli4ai. We built a package manager specifically for AI CLI tools—like npm, but for tools that AI agents can use.

Setting Up CLI Tools for AI: A Practical Example

Let’s walk through giving an AI agent access to GitHub operations.

Step 1: Install the Package

npm install -g cli4ai
cli4ai add github

Step 2: Configure Authentication

# Set your GitHub token
cli4ai secret set GITHUB_TOKEN ghp_xxxxxxxxxxxx

Secrets are stored securely and injected at runtime—they’re never exposed to the AI.

Step 3: Use as CLI or MCP Server

As a CLI tool (for testing or scripting):

cli4ai run github search "machine learning language:python"

As an MCP server (for AI integration):

cli4ai start github

This starts an MCP server that any compatible AI client can connect to.

Step 4: Connect to Your AI

In Claude Desktop, add to your config:

{
  "mcpServers": {
    "github": {
      "command": "cli4ai",
      "args": ["start", "github"]
    }
  }
}

Now Claude can search repositories, create issues, review PRs, and more—all through natural language.

Security Considerations

Giving AI access to CLI tools introduces security concerns. Here’s how to address them:

Principle of Least Privilege

Only expose the operations AI actually needs. Use scope restrictions to limit what tools can do:

# Run with read-only scope
cli4ai run github repos --scope read

Secret Management

Never pass secrets through prompts or environment variables visible to the AI:

# Secrets are injected by the runtime, not passed through prompts
cli4ai secret set API_KEY xxx

Sandboxing

Consider running tools in isolated environments:

# Run with sandbox restrictions
cli4ai run github repos --sandbox

Audit Logging

Track what operations AI performs:

# Enable audit logging (enabled by default)
cli4ai config audit.enabled true

# Logs are written to ~/.cli4ai/logs/mcp-audit-{date}.log

Building Your Own AI-Accessible Tool

Want to wrap your own CLI tool for AI access? Here’s the structure:

my-tool/
├── cli4ai.json      # Package manifest
├── commands/
│   ├── search.ts    # Command implementation
│   └── create.ts
└── package.json

The manifest defines the AI-facing interface:

{
  "name": "my-tool",
  "version": "1.0.0",
  "description": "My custom tool for AI",
  "commands": {
    "search": {
      "description": "Search for items",
      "args": [
        {
          "name": "query",
          "type": "string",
          "description": "Search query",
          "required": true
        }
      ]
    }
  }
}

Each command outputs JSON to stdout for AI consumption:

import { output, log } from '@cli4ai/lib';

export default async function search(args) {
  log('Searching...');  // Progress goes to stderr

  const results = await performSearch(args.query);

  output(results);  // JSON goes to stdout for AI
}

Check out our guide on creating packages for the full walkthrough.

What’s Next for AI + CLI Tools

The space is evolving rapidly. Here’s what we’re seeing:

More sophisticated tool use: AI agents are getting better at chaining multiple tools together to accomplish complex tasks.

Standardization around MCP: As the Model Context Protocol gains adoption, we’ll see more interoperability between AI platforms and tools.

Domain-specific tool suites: Expect curated collections of tools for specific workflows—DevOps, data science, content creation.

Better permission models: Fine-grained control over what AI can and can’t do with each tool.

Conclusion

The best way to give CLI tools to AI agents in 2025 is through structured, schema-driven interfaces—ideally using the Model Context Protocol. This gives you:

• Security: Controlled, validated operations
• Discoverability: AI knows what tools are available
• Maintainability: Standard interfaces that work across platforms
• Composability: Mix and match tools as needed

Whether you’re building AI workflows for development, operations, or automation, giving your agents access to CLI tools dramatically expands what they can accomplish.

Ready to get started? Install cli4ai and give your AI agents superpowers in minutes.