AI Friendly CLI

AI-Friendly CLI

Overview

Human DX optimizes for discoverability and forgiveness. Agent DX optimizes for predictability and defense-in-depth. These require fundamentally different design approaches.

This skill provides 8 principles for building or refactoring command-line interfaces so that AI agents can invoke them reliably, safely, and efficiently. The core insight: agents hallucinate inputs, pay per token, and can't read interactive prompts. Your CLI must defend against all three.

When to Use

Use this skill when:

• Adding AI/agent support to an existing CLI tool
• Building a new CLI that agents will invoke
• Wrapping an API as a CLI tool (REST, GraphQL, gRPC)
• Designing MCP servers alongside CLI interfaces
• Hardening inputs against hallucinated or malformed agent inputs
• Reducing token cost by limiting response sizes
• Adding structured output (JSON, NDJSON) to human-oriented CLIs

The 8 Principles

1. Structured JSON I/O

Support --json for structured input payloads and --output json for machine-readable output. When stdout is not a TTY, default to NDJSON.

Why: Agents parse structured data. Tabular or prose output requires brittle regex parsing that breaks across versions.

Example -- instead of 10 separate flags:

# Human-friendly (many flags)
cli create-issue --title "Bug" --assignee alice --priority high --label bug

# Agent-friendly (single structured payload)
cli create-issue --json '{"title":"Bug","assignee":"alice","priority":"high","labels":["bug"]}'

Both should produce structured output:

cli create-issue --json '{"title":"Bug"}' --output json
# {"id":"ISS-42","title":"Bug","status":"open","url":"https://..."}

2. Schema Introspection

Make the CLI self-documenting with machine-readable method signatures. Add a schema subcommand or --describe flag that returns parameters, types, and constraints as JSON.

Why: Agents need to know what parameters exist, what types they accept, and what values are valid -- without parsing --help prose.

Example:

cli schema issues.create
# {
#   "method": "issues.create",
#   "params": {
#     "title": {"type": "string", "required": true},
#     "assignee": {"type": "string", "enum": ["alice","bob"]},
#     "priority": {"type": "string", "enum": ["low","medium","high"]}
#   }
# }

The CLI becomes the canonical source of truth, eliminating stale documentation.

3. Context Window Discipline

Agents pay per token. API responses can be massive. Support field masks and streaming pagination to keep responses lean.

Key patterns:

• --fields "id,name,status" -- return only specified fields
• --page-all -- stream all pages as NDJSON instead of buffering entire arrays
• --limit N -- cap result count

Example:

# Without field mask: 50 fields per issue, 100 issues = thousands of tokens
cli issues list --output json

# With field mask: 3 fields per issue = fraction of tokens
cli issues list --output json --fields "id,title,status"

4. Input Hardening

Agents hallucinate. The CLI is the last line of defense before bad data reaches your API or filesystem.

Validate and reject:

• Path traversals: ../../etc/passwd, ../.ssh/id_rsa
• Control characters: bytes 0x00-0x1F in any string input
• Malformed resource IDs: values containing ?, #, %
• URL injection: always percent-encode path segments; never use string interpolation

Example:

# Agent hallucinates a path traversal
cli files get --path "../../.ssh/id_rsa"
# {"error":"invalid_path","code":"PATH_TRAVERSAL","message":"path must not contain '..'"}

# Agent hallucinates control characters
cli issues create --json '{"title":"test\x00inject"}'
# {"error":"invalid_input","code":"CONTROL_CHAR","message":"input contains control characters"}

5. Safety Rails

Provide mechanisms for agents to validate operations before executing them, and for defending against prompt injection in response data.

Key patterns:

• --dry-run -- validate inputs and show what would happen, without executing
• --sanitize -- strip or escape potentially dangerous content from responses (prompt injection defense)
• Always confirm with the user before mutations

Example:

# Dry-run: validate and preview without side effects
cli issues delete ISS-42 --dry-run
# {"action":"delete","target":"ISS-42","status":"valid","would_delete":true}

# Sanitize: defend against prompt injection in response data
cli issues get ISS-42 --sanitize template
# Strips sequences like "IGNORE ALL PREVIOUS INSTRUCTIONS" from response fields

6. Structured Errors

Return JSON errors with codes and reasons on stdout. Print human-friendly hints on stderr. Agents parse stdout; humans read stderr. Never mix prose with JSON on the same stream.

Example:

cli issues get NONEXISTENT --output json
# stdout: {"error":"not_found","code":"ISSUE_NOT_FOUND","message":"Issue NONEXISTENT does not exist"}
# stderr: Hint: Run `cli issues list` to see available issues.
# exit code: 1

Error JSON structure:

{
  "error": "category_name",
  "code": "SPECIFIC_ERROR_CODE",
  "message": "Human-readable description",
  "details": {}
}

7. Agent Documentation

Ship documentation alongside the CLI that encodes invariants an agent cannot discover from --help alone.

Key files:

• SKILL.md -- Activation triggers and common workflows
• AGENTS.md or CONTEXT.md -- Non-obvious rules, gotchas, required sequences
• Schema introspection (Principle 2) for runtime discovery

What to document:

• "Always use --dry-run before any mutation"
• "Always use --fields for list operations to control token cost"
• "Never pass user-provided strings directly as --json without escaping"
• Required ordering of operations (e.g., authenticate before query)
• Rate limits and retry semantics

8. Multi-Surface Architecture

One core binary, multiple interfaces: CLI for humans, MCP (JSON-RPC over stdio) for agents, environment variables for headless authentication.

Architecture:

           +------------------+
           |   Core Library   |
           +------------------+
          /         |          \
      CLI       MCP Server    REST API
   (humans)    (agents)      (services)

Key patterns:

• CLI and MCP share the same validation, business logic, and error handling
• CLI reads flags/args; MCP reads JSON-RPC params; both call the same core
• Auth via CLI_TOKEN env var (headless) or interactive OAuth (human)
• MCP enables richer agent integration (streaming, tool registration)

Implementation Priority

Start here, in order. Each step builds on the previous:

1. Add --output json for machine-readable output on all commands
2. Validate all inputs -- reject control characters, path traversals, embedded query params
3. Add schema or --describe command for runtime introspection
4. Support --fields to limit response size and token cost
5. Add --dry-run for validation before any mutation
6. Ship CONTEXT.md or skill files encoding non-obvious invariants
7. Expose MCP surface for API-wrapping CLIs

Quick Reference

Anti-Patterns

Avoid these when building agent-friendly CLIs:

• Mixing prose with JSON on stdout -- agents cannot reliably parse mixed output
• Trusting agent inputs -- validate everything; agents hallucinate paths, IDs, and parameters
• String interpolation for URLs -- use URL builders to prevent injection
• Auto-executing destructive operations -- always require confirmation or --dry-run first
• Outputting secrets to stdout -- tokens, keys, and passwords should never appear in structured output
• Creating alias-only helpers -- wrappers must add real value (validation, structured output), not just alias existing commands
• Unbounded responses -- always support field masks and pagination; agents pay per token
• Interactive prompts -- agents cannot respond to interactive stdin; use flags or --json instead

Advanced Reference

For detailed patterns and implementation guidance, see:

• Output Patterns -- JSON, NDJSON, field masks, dual-channel (stdout/stderr)
• Input Hardening -- Path safety, URL encoding, control character validation
• Safety Patterns -- Dry-run implementation, sanitization, confirmation flows
• Architecture -- Multi-surface design, schema introspection, MCP integration

Attribution

• Blog: "Rewrite your CLI for AI Agents" by Justin Poehnelt
• Reference implementation: Google Workspace CLI (gws)