
HUMAN:
Brainstorm with me: A dynamic CLI what defines itself based on a cloud configuration and support multiple OPENAPI specs, MCP servers and custom integrations

Example:

"supercli referential users fetch" => fetch users

Flow:

checks config locally TTL cache
fetch config from cloud

AI:
You’re essentially describing a **cloud-defined CLI runtime**.
The CLI itself is just a **thin execution engine**, while **commands are dynamically materialized from configuration** that maps to APIs, MCP tools, or integrations.

Below is a structured brainstorming.

---

# 1. Mental Model

Think of it as:

**CLI = runtime + interpreter for a command graph**

The cloud config defines:

* namespaces
* commands
* arguments
* bindings to execution backends

Example command:

```
supercli referential users fetch --id 42
```

Internally resolves to something like:

```
namespace: referential
resource: users
action: fetch
```

Which maps to an execution adapter:

```
adapter: openapi
spec: referential-api
operationId: getUser
```

---

# 2. Core Components

### 1️⃣ CLI Runtime

Responsibilities:

* parse commands
* resolve config
* route execution
* handle auth
* render output

Think:

```
supercli
 ├── parser
 ├── config-loader
 ├── executor
 └── adapters
```

---

### 2️⃣ Cloud Command Registry

A **central configuration service** describing commands.

Example config:

```yaml
namespaces:
  referential:
    users:
      fetch:
        adapter: openapi
        spec: referential-api
        operationId: getUser
      list:
        adapter: openapi
        spec: referential-api
        operationId: listUsers

  ai:
    summarize:
      adapter: mcp
      server: ai-tools
      tool: summarize
```

---

### 3️⃣ Adapter Layer

Each backend type has a runtime adapter.

Adapters:

```
openapi
mcp
http
graphql
custom-js
shell
grpc
```

Interface:

```
execute(command, args, context)
```

---

# 3. Command Resolution Pipeline

Example:

```
supercli referential users fetch --id 12
```

Pipeline:

1️⃣ Parse command

```
["referential", "users", "fetch"]
```

2️⃣ Load cached config

```
~/.supercli/cache/config.json
```

3️⃣ TTL expired?

```
yes → fetch cloud config
no → use local
```

4️⃣ Resolve command

```
referential.users.fetch
```

5️⃣ Select adapter

```
openapi
```

6️⃣ Execute

```
operationId=getUser
```

---

# 4. Local Cache Strategy

Cache file:

```
~/.supercli/config-cache.json
```

Example metadata:

```
{
  "version": "d81a9c",
  "ttl": 3600,
  "fetched_at": 171000000
}
```

Startup flow:

```
if cache.valid():
   use cache
else:
   fetch cloud config
```

Optional:

```
supercli config refresh
```

---

# 5. OPENAPI Integration

When registering a spec:

```
specs:
  referential-api:
    type: openapi
    url: https://api.company.com/openapi.json
```

CLI dynamically maps operations:

Example mapping:

```
referential users fetch -> GET /users/{id}
referential users list -> GET /users
```

Arguments inferred from:

```
parameters
requestBody
```

So the CLI can auto-generate:

```
--id
--limit
--offset
```

---

# 6. MCP Server Integration

Config:

```
mcp_servers:
  ai-tools:
    url: https://mcp.company.com
```

Command:

```
ai summarize
```

Mapping:

```
tool: summarize
```

Execution:

```
call MCP tool
```

---

# 7. Custom Integration Example

Example adapter:

```
adapter: shell
command: terraform
args:
  - apply
```

Or:

```
adapter: node
handler: deployLambda
```

---

# 8. Plugin System

Allow extensions:

```
~/.supercli/plugins/
```

Plugin registers:

```
adapter
middleware
auth provider
output formatter
```

---

# 9. Smart CLI UX

Dynamic help:

```
supercli
```

Output:

```
Available namespaces:

referential
ai
billing
infra
```

Then:

```
supercli referential
```

Output:

```
users
groups
permissions
```

Then:

```
supercli referential users
```

Output:

```
fetch
list
create
delete
```

Everything **generated from config**.

---

# 10. Authentication Model

Auth providers:

```
oauth
apiKey
jwt
aws-sigv4
```

Config example:

```
auth:
  provider: oauth
  token_url: https://auth.company.com/token
```

Stored locally:

```
~/.supercli/credentials
```

---

# 11. Output Formatting

Support:

```
--json
--yaml
--table
--raw
```

Example:

```
supercli referential users list --table
```

---

# 12. Interesting Advanced Features

### Command Graph

Allow:

```
supercli run workflow.yaml
```

Example:

```
steps:
  - referential.users.fetch
  - ai.summarize
```

---

### AI Assisted CLI

```
supercli ask "fetch user 42 and summarize activity"
```

LLM maps to:

```
referential.users.fetch
ai.summarize
```

---

### Auto-generated SDK

Same config could generate:

```
CLI
SDK
Docs
```

---

# 13. Real Value

This becomes:

**“Postman + CLI + API gateway + MCP router”**

But **cloud-driven**.

Use cases:

* internal platform engineering
* developer portals
* infra automation
* AI tool orchestration

---

# 14. Name Ideas

If productized:

```
SUPERCLI
CommandMesh
Clousupercli
OpenCommand
CmdFabric
ControlPlane CLI
```

---

# 15. Interesting Architectural Twist

Instead of commands being static:

**commands are nodes in a graph**

Example:

```
referential.users.fetch
```

is actually:

```
namespace/resource/action
```

Meaning you could also support:

```
supercli referential.users.fetch
```

or

```
supercli run referential.users.fetch
```

---

# 16. Killer Feature

One idea that makes it **really powerful**:

**commands can compose other commands**

Example:

```
deploy service
```

Internally runs:

```
infra.build
infra.push
infra.deploy
infra.verify
```

---

💡 My take:
If you add **OpenAPI + MCP + workflows**, this becomes a **universal operational CLI for AI-era infrastructure**.

Which is actually a **very strong developer tool category**.

---

If you want, I can also show you a **clean architecture for implementing this in ~2k LOC (Go or Node)** that would make a **very powerful POC quickly.**
