Plugin vs Skill vs MCP vs API

Agent Analytics exposes one analytics surface through five real access paths:

Plugin for Claude Code when you want the MCP connection and analytics workflow layer bundled together
Skill for agent environments that already support skills and command execution
MCP for chat-native and editor-native tool use
CLI for shell-oriented agent workflows
API for raw HTTP control

The product model does not change between them. Projects, analytics reads, and experiment operations stay the same; only the native entrypoint changes.

Recommended path by environment

Environment	Recommended path	Why
Claude Code	Plugin first	Shortest hosted path with both MCP connectivity and Agent Analytics workflow guidance
Claude Desktop / Cowork	Hosted MCP	Best fit for connector-style chat tools with native tool calls
Cursor	Skill + CLI first	Usually lower overhead than MCP when the agent can already run commands
OpenAI Codex	Skill first	Keeps the workflow agent-native without requiring MCP
OpenClaw	Skill first	Cleanest path when OpenClaw owns the scheduled analytics job from chat
Custom runtime or internal agent	API	Best fit when you own retries, parsing, and orchestration

When to use each path

Plugin

Use the plugin when your environment is Claude Code and you want one install that packages both:

the hosted MCP connection
the analytics-specific workflow layer

This is the cleanest default when the plugin marketplace is available.

Skill

Use a skill when your agent already supports skills and can execute commands in the same environment.

A skill is usually the best fit when:

you want a guided workflow layer around common analytics tasks
your agent already has terminal access
you want to stay in the agent’s native loop instead of switching to tool-call-heavy MCP flows

MCP

Use MCP when your AI agent already runs inside a tool that supports connectors or MCP servers.

MCP is usually the best fit when:

you want the install to feel native inside chat
you want tool calls instead of shell commands
you do not want to hand-roll auth headers or request payloads
you want quick project or account summaries through structured tool responses
you want agent-readable reports such as analytics_paths, where the tool response includes both compact text and structured data

Tradeoff:

MCP often adds more latency and token overhead than skill + CLI flows because the model has to manage more tool-call round trips and tool result payloads.

CLI

Use the CLI when your AI agent already has terminal access and is comfortable executing commands.

CLI is usually the best fit when:

your AI agent already lives in a shell-first environment
you want predictable command output
you prefer command composition over tool integration
you want lower overhead than MCP in editor-style agents like Cursor
you want simple local auth helpers like login and logout around the same API
you want shell-readable commands such as paths that summarize entry pages, exit pages, terminal labels, and next-step analysis

For install, login flow, common commands, and CLI-to-API mapping, continue to the dedicated CLI page.

API

Use the API when you want strict control over requests, retries, and response parsing.

API is usually the best fit when:

you are integrating from your own code
you need exact HTTP-level behavior
you are debugging auth or payload shape directly

Quick rule of thumb

Choose plugin first in Claude Code when the marketplace path is available.
Choose skill + CLI first in shell-capable environments like Cursor or Codex.
Choose MCP when the agent already lives in a connector-style chat environment and you want native tool calls.
Choose API when you need full control, custom integration, or lower-level debugging.

Paths Across Access Paths

Session paths are available through the same product surface:

CLI: agent-analytics paths <project> --goal <event>
MCP: analytics_paths
API: POST /paths

Use paths when the agent needs to connect entry pages and exit pages to goal behavior before deciding whether to run a funnel query, retention check, or experiment.

The report is intentionally bounded and session-local. It is not a long-cycle identity-stitching report.