openapi-cli4ai 0.2.0

Turn any REST API with an OpenAPI spec into an AI-ready CLI

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Let your AI coding agent (Claude Code, Cursor, Codex, Copilot) talk to any REST API. No MCP servers. No schemas in the prompt. Just point it at a URL and let it fly.

Install

# Requires uv (https://docs.astral.sh/uv/)
uv pip install openapi-cli4ai

# Or run directly without installing
uvx openapi-cli4ai --help

Quick Start

# Point it at any API with an OpenAPI spec
openapi-cli4ai init petstore --url https://petstore3.swagger.io/api/v3

# Discover endpoints
openapi-cli4ai endpoints

# Search endpoints
openapi-cli4ai endpoints -s pet

# Run an operation by name (inputs auto-routed from the spec)
openapi-cli4ai run findPetsByStatus --input '{"status": "available"}'

# Or call an endpoint directly
openapi-cli4ai call GET /pet/findByStatus --query status=available

How It Works

Your AI Agent (Claude, GPT, Cursor, etc.)
        |
        |  1. "endpoints -s users"     <-- discover what's available
        |  2. reads the endpoint list
        |  3. "call GET /users/123"    <-- makes the API call
        |
        v
+---------------------+         +----------------------+
|   openapi-cli4ai    |-------->|   Your API Server    |
|                     |         |   (any REST API)     |
|  * Fetches spec     |<--------|                      |
|  * Caches it        |         +----------------------+
|  * Routes calls     |
|  * Handles auth     |
+---------------------+

The agent uses endpoints to discover what's available, then call to hit the right endpoint with the right parameters. That's the entire integration. The agent already knows how to use a CLI — you just give it this one and point it at your API.

Why Not MCP?

Every MCP tool you connect injects its schema into the prompt. Every parameter, every description, every type definition. Connect a few MCP servers and your agent is burning tokens on tool descriptions before it even starts thinking about your task.

For large API surfaces, there's a better pattern: pull-based discovery.

Nothing is injected into the prompt upfront. The agent pulls what it needs, when it needs it. It sees a compact endpoint index, picks the right endpoint, and calls it. This works with APIs of any size — including 10MB+ specs — without stuffing everything into context.

This isn't MCP vs. OpenAPI. It's about having both tools and reaching for the right one. For quick integrations, MCP is great. For large or unfamiliar API surfaces, an OpenAPI spec and a thin CLI is the better play.

Commands

init — Point it at an API

# Auto-detect OpenAPI spec location
openapi-cli4ai init myapi --url https://api.example.com

# Specify spec path
openapi-cli4ai init myapi --url https://api.example.com --spec /v2/openapi.json

# Specify auth type
openapi-cli4ai init myapi --url https://api.example.com --auth bearer

# Use a remote spec URL
openapi-cli4ai init myapi --url https://api.example.com --spec-url https://example.com/spec.json

# Skip SSL verification (internal/staging APIs)
openapi-cli4ai -k init myapi --url https://staging.internal.example.com

run — Run an operation by name

# Query parameter — auto-routed from the spec
openapi-cli4ai run findPetsByStatus --input '{"status": "available"}'

# Path parameter — substituted into the URL
openapi-cli4ai run getPetById --input '{"petId": 123}'

# Request body — keys not matching a parameter go to the body
openapi-cli4ai run addPet --input '{"name": "Rex", "status": "available"}'

# Load input from a file
openapi-cli4ai run addPet --input-file pet.json

# Raw JSON output
openapi-cli4ai run getInventory --json

The spec tells the tool where each parameter goes (path, query, header, body). You just pass a flat JSON object.

endpoints — Discover API endpoints

# List all endpoints
openapi-cli4ai endpoints

# Search by keyword
openapi-cli4ai endpoints -s pet

# Filter by tag
openapi-cli4ai endpoints --tag store

# Output as JSON (useful for AI agents)
openapi-cli4ai endpoints --format json

# Compact one-line-per-endpoint view
openapi-cli4ai endpoints --format compact

call — Call any endpoint

# GET request
openapi-cli4ai call GET /pet/findByStatus --query status=available

# POST with JSON body
openapi-cli4ai call POST /pet --body '{"name": "Rex", "status": "available"}'

# POST with body from file
openapi-cli4ai call POST /pet --body @payload.json

# Multiple query parameters
openapi-cli4ai call GET /pet/findByStatus --query status=available --query limit=10

# Custom headers
openapi-cli4ai call GET /resource --header "X-Custom:value"

# Stream SSE responses
openapi-cli4ai call POST /chat --body '{"message": "hello"}' --stream

# Raw output (no formatting)
openapi-cli4ai call GET /pet/1 --raw

profile — Manage API profiles

# List profiles
openapi-cli4ai profile list

# Switch active profile
openapi-cli4ai profile use myapi

# Show profile config
openapi-cli4ai profile show

# Add a profile manually
openapi-cli4ai profile add myapi --url https://api.example.com

# Remove a profile
openapi-cli4ai profile remove myapi

login / logout — Authentication

# Login (for APIs with OAuth/token endpoints)
openapi-cli4ai login --username admin

# Login with password from file (for automation)
openapi-cli4ai login --username admin --password-file /path/to/secret

# Login with password from stdin
echo "my-password" | openapi-cli4ai login --username admin --password-stdin

# Logout (clear cached tokens)
openapi-cli4ai logout

Configuration

Profiles are stored in ~/.openapi-cli4ai.toml. Secrets are referenced via environment variables — never stored in the config file.

active_profile = "myapi"

[profiles.myapi]
base_url = "https://api.example.com"
openapi_path = "/openapi.json"

[profiles.myapi.auth]
type = "bearer"
token_env_var = "MYAPI_TOKEN"

[profiles.stripe]
base_url = "https://api.stripe.com"
openapi_url = "https://raw.githubusercontent.com/stripe/openapi/master/openapi/spec3.json"

[profiles.stripe.auth]
type = "api-key"
env_var = "STRIPE_SECRET_KEY"
header = "Authorization"
prefix = "Bearer "

[profiles.internal-app]
base_url = "http://localhost:8000"
verify_ssl = false

[profiles.internal-app.auth]
type = "bearer"
token_endpoint = "/api/auth/token"
refresh_endpoint = "/api/auth/refresh"

[profiles.internal-app.auth.payload]
username = "{username}"
password = "{password}"
tenant = "{env:MY_TENANT}"
account_type = "USERNAME"

Payload placeholders: {username} and {password} come from the login prompt. {env:VAR_NAME} pulls from environment variables or a .env file (loaded automatically).

Auth Types

Type	Use Case	Config Fields
none	Public APIs	—
bearer	Token from env var	token_env_var
bearer	OAuth token endpoint	token_endpoint, refresh_endpoint, payload
api-key	API key in header	env_var, header, prefix
basic	HTTP Basic auth	username_env_var, password_env_var

Tested With

API	Endpoints	Auth	Example
Petstore	19	None	init petstore --url https://petstore3.swagger.io/api/v3
NWS Weather	60	None	init nws --url https://api.weather.gov
PokéAPI	97	None	init pokeapi --url https://pokeapi.co --spec-url ...
D&D 5e	47	None	init dnd --url https://www.dnd5eapi.co --spec-url ...
Nager.Date Holidays	8	None	init holidays --url https://date.nager.at --spec /openapi/v3.json
GitHub	1,080	Bearer	init github --url https://api.github.com --spec-url ... --auth bearer
Jira Cloud	581	Basic	init jira --url https://your-domain.atlassian.net --spec-url ... --auth basic
OpenRouter	36	Bearer	init openrouter --url https://openrouter.ai/api/v1 --spec-url ... --auth bearer
DBGorilla	500+	Token endpoint	See examples/profiles.toml.example

Works with any AI agent that has shell access — Claude Code, Cursor, GitHub Copilot, or anything that can run endpoints and call.

Claude Code Setup

Add this to your global ~/.claude/CLAUDE.md so every Claude Code session knows about the tool — regardless of which repo you're working in:

## API Access

You have `openapi-cli4ai` installed for interacting with REST APIs.

- Discover endpoints: `openapi-cli4ai endpoints -s <keyword>`
- Call endpoints: `openapi-cli4ai call <METHOD> <path> [--query key=value] [--body '{}']`
- List profiles: `openapi-cli4ai profile list`
- Switch profile: `openapi-cli4ai profile use <name>`
- Login (if needed): `openapi-cli4ai login`

Use `openapi-cli4ai endpoints` to explore before making calls.
Use `--format json` when you need to parse the output programmatically.
Use `-k` flag for APIs with self-signed or internal certificates.

This works because ~/.claude/CLAUDE.md is loaded into every conversation context, not just the current repo.

Development

git clone https://github.com/dbgorilla/openapi-cli4ai.git
cd openapi-cli4ai
uv pip install -e .
pytest tests/ -m "not integration" -v

Security

• All credentials stay local — profiles reference env var names, not values
• Token cache protected with restricted file permissions (0600)
• PyPI releases use Trusted Publishers with Sigstore attestation
• All CI actions pinned to commit SHAs
• Dependabot monitors dependencies weekly
• See SECURITY.md for vulnerability reporting

Attribution

This project was inspired by @tomleavy, who originated the idea that an OpenAPI spec and a thin CLI is all an AI agent needs to talk to any API.

License

MIT — see LICENSE.