MCP vs CLI vs Skill vs REST API for Social Publishing

MCP, CLI, Skill, and REST API are not interchangeable versions of one social publishing interface. They occupy different layers. A Skill gives an AI agent operating instructions and installs the CLI behind them. The CLI gives a person or shell-capable agent a native command surface. MCP exposes remote tools to a compatible AI client. The REST API lets application code control publishing operations directly.

The choice depends on who operates the workflow and where it must run. Use a Skill or MCP for agent-led interaction, the CLI for shell-based work, and REST for application-owned orchestration. If those needs change between development, approval, and production, use more than one route.

Decision matrix

Route	Primary operator	Best use	Authentication	Discoverability	Automation fit	Main limitations
Skill	An AI agent that can install and read Skills	Guided, conversational publishing from a supported coding or agent client	The Skill uses the CLI behind the scenes; authentication depends on the environment and CLI path	High at the instruction level: the agent receives the commands and operating guidance	Good for agent-led tasks where the model decides which commands to run	Requires Skill support. Sandboxed clients can impose network and persistence constraints
CLI	A person, script, or agent with shell access	Local workflows, terminal automation, and explicit command execution	Browser device-flow login with no API key required for that login path	Explicit rather than automatic: the operator or agent must know the commands	Good when the runtime has a persistent shell and can invoke a binary	Not available to clients without shell access; command knowledge must come from the operator, a Skill, or other instructions
MCP	An AI client that supports remote HTTP MCP servers	Tool calling inside an agent conversation or workspace	Bearer header, API key in the URL, or OAuth 2.0	High in compatible clients because server tools can be exposed to the agent	Good for interactive and delegated agent workflows	Client support and authentication configuration vary; some clients have no confirmed Groniz MCP path
REST API	An application, service, or custom agent runtime	Deterministic backend workflows and product integrations	Bare `Authorization: your-api-key` header, without `Bearer`	Contract-led: developers work from API operations rather than agent-discovered tools	Good for application-owned scheduling, media, and lifecycle logic	You own the orchestration, error handling, secrets, and mapping from agent intent to API calls

The matrix is a starting point, not a ranking. "Agent-led" and "application-owned" are more useful distinctions here than "easy" and "advanced."

What each route controls

Skill: instructions plus the CLI

The Groniz Skill teaches an AI agent the relevant commands and installs the Groniz CLI behind them. It suits an operator who wants to request an outcome in natural language and let the agent select the commands.

The standard install for Claude Code, Codex, ChatGPT, OpenCode, and OpenClaw is:

npx skills add groniz/groniz-cli

The Skill still inherits the constraints of the client running it. ChatGPT Skills are available only on Business, Enterprise, Healthcare, and Edu. The ChatGPT sandbox has internet access off by default and resets between sessions, so a later session may need the environment set up again. ChatGPT does not have a confirmed Groniz MCP path, so the Skill is its documented agent route.

A Skill is not a separate publishing transport. It is an agent-facing instruction layer over the CLI. To audit the execution boundary, inspect the commands the agent will run and the environment where the CLI runs.

CLI: a native command boundary

The Groniz CLI is a self-contained native binary, and it does not require Node. It fits workflows in which a person, automation script, or AI agent has access to a persistent shell. Install it and log in with:

curl -fsSL https://groniz.com/install.sh | sh
groniz auth:login

groniz auth:login uses a browser device flow. This login path does not require an API key, so it can suit a developer workstation where a human can complete the browser step. A service that receives an injected API key uses a different operational model.

The CLI can also serve as the execution boundary for an agent workflow. The agent proposes or runs a specific command, while an operator or policy layer controls shell permissions. Client compatibility limits this approach. Claude's app and Claude Cowork cannot access the local shell, so they use MCP instead of the CLI. NanoClaw has the opposite constraint: its documented path is CLI-only because it supports only stdio MCP, while the Groniz MCP server uses remote HTTP.

For a worked example of a shell-capable agent workflow, see how to automate social media posting with Codex.

MCP: remote tools inside an AI client

Groniz exposes a remote MCP server at:

https://mcp.groniz.com/mcp

MCP fits an AI client that should see publishing operations as tools and choose among them during a conversation or delegated task. The tool surface remains inside the client, so the model does not have to construct shell commands or HTTP requests.

Authentication can use an Authorization: Bearer YOUR_API_KEY header, an API key in the server URL, or OAuth 2.0. The correct setup depends on the client. For example, Codex requires the bearer-token environment variable to be set before Codex launches; placing a plaintext token in its configuration is not the supported path.

Compatibility decides whether this route is available. Claude's app and Claude Cowork are MCP-only for Groniz. ChatGPT has no confirmed Groniz MCP path. NanoClaw is CLI-only. Other clients may support remote MCP but handle headers, OAuth, or environment variables differently. Check the client's supported connection model before choosing authentication.

If MCP is the route you are evaluating, how social media MCP servers work covers the broader tool and publishing model.

REST API: an application contract

The public REST API lives under api.groniz.com/public/v1/*. It supports listing integrations, finding the next slot, uploading media, and scheduling, listing, and deleting posts. Use REST when your own service, job runner, or agent platform owns the publishing lifecycle.

REST authentication differs from MCP authentication. The public API expects the API key as the bare value of the header:

Authorization: your-api-key

There is no Bearer prefix on that REST header. Reusing an MCP header unchanged is therefore a configuration error, even though both routes may use the same kind of secret.

REST gives the application explicit control and puts more responsibility in application code. Your system must decide when to call each operation, protect the key, manage uploads, record post state, and handle failures or deletion. Unlike MCP, REST does not rely on an AI client to discover tools and translate intent into a call.

Choose by operating model

Work through these questions in order:

Who initiates the action? If an AI agent receives conversational tasks, start with Skill or MCP. If a service reacts to an event or queue, start with REST. If a person or shell-capable agent works in a terminal, start with the CLI.
What can the runtime access? A client without shell access rules out CLI and Skill-over-CLI. A runtime without remote HTTP MCP support rules out Groniz MCP. A custom backend can call REST directly.
How should credentials enter the system? A developer workstation can use CLI device-flow login. MCP supports Bearer, URL-key, and OAuth options, subject to the client. REST uses the bare Authorization header.
Where should tool selection happen? A Skill supplies command guidance to the model. MCP presents tools in a compatible client. CLI automation selects commands explicitly. REST code selects endpoints explicitly.
Who owns recovery and state? With REST, your application clearly owns orchestration. With agent-led routes, define what the agent may retry, delete, or schedule and where an operator must approve.

This route comparison is narrower than choosing a social publishing layer for an AI agent. The layer decision includes governance and delivery design. The route decision concerns how the chosen runtime reaches Groniz.

Hybrid approaches that preserve clear boundaries

You do not need one route for every stage.

Skill plus CLI is the built-in pairing. The Skill teaches the agent what to do, and the CLI is the executable surface. This combination suits supported clients with shell access and keeps agent instructions separate from the transport protocol.

MCP for operator-led work plus REST for backend jobs separates exploration from production orchestration. An operator can inspect integrations and prepare or schedule work through an MCP-capable client, while a service uses REST for application-owned flows. The two paths should share an approval policy and a record of what was scheduled.

CLI for development plus REST for deployed automation gives each environment its own route. A developer can authenticate through the browser device flow and exercise a workflow locally, while the deployed service uses an API key and explicit REST calls. Treat those as different authentication contexts rather than assuming local credentials transfer to production.

Different clients may require different routes. A team using Claude Cowork and NanoClaw cannot standardize on one Groniz route: Cowork uses MCP, while NanoClaw uses the CLI. Standardize the publishing policy, integration selection, and evidence you record instead of forcing identical transport.

The route does not erase channel differences

Groniz Connectors covers 32+ networks, but providers do not expose identical formats, media options, analytics, or scheduling capabilities. A payload that works for one connected account may fail for another. Check the live integration schema before posting, whether the request begins in a Skill, CLI command, MCP tool call, or REST workflow.

That check belongs near the publishing action, not only in initial setup. Integrations and provider capabilities are operational inputs. The broader AI-agent social media publishing lifecycle explains how schema checks fit with drafting, approval, delivery, and verification.

There is no universal winner in MCP vs CLI social publishing. Pick the route that matches the runtime's actual access, the operator who owns the action, and the place where orchestration should live. When those answers differ by stage, use a hybrid and keep authentication, approval, and delivery records explicit.

Compare the documented paths for your client on the Groniz agents page.