# AI Agent vs Skill vs MCP vs API — What Each Layer Actually Does UnifAPI · 2026-08-03 UnifAPI's episode on AI Agent vs Skill vs MCP vs API argues the four are separate layers in a public-data workflow, each owning a different job. It defines an API as the contract returning structured public records, MCP as the assistant-friendly connection path for Claude, ChatGPT, Codex, or other clients, a Skill as a repeatable task playbook, and an Agent as the user-facing role and decision loop. Using an Instagram creator example, a request moves down through agent, skill, MCP and API to gather live public evidence and returns as a cited answer. It stresses UnifAPI's scope is read-only: no posting, DMs, or private accounts, and directs users to Agents for an outcome, Skills for a workflow, MCP to connect an assistant, or the API catalog for custom integrations. ## Questions this episode answers ### What is the difference between an AI agent, a skill, MCP, and an API? Speaker 1 explains that each layer has a distinct job. An API is the contract that returns structured public records. MCP sits above as a standard connection path for assistants like Claude or ChatGPT. A skill is the repeatable playbook that defines a concrete job, guiding data collection and comparison. An agent is the user-facing role and decision loop that understands the outcome, selects skills, and suggests next steps. Together they form a stack moving from data to decision. [0:01](https://unifapi.podhood.com/673232b3-412f-4256-8ecb-9c8b40e5247a?t=1000) ### How would I use UnifAPI to find Instagram creators for a product launch? Speaker 1 explains that you could ask an assistant like Claude. Underneath, an API returns public records such as profiles, posts, and comments. MCP makes these callable. A skill acts as a playbook, setting audience-fit criteria, inspecting content, and flagging brand safety risks. The agent then uses the skill to produce a ranked brief with evidence, helping you decide which creators fit your campaign. [0:18](https://unifapi.podhood.com/673232b3-412f-4256-8ecb-9c8b40e5247a?t=18000) ### Do UnifAPI agents have access to private social accounts or can they post? Speaker 1 states that UnifAPI agents today perform read-only research. They can analyze public data and draft recommendations, but they do not post content, send direct messages, or access private upstream social accounts. This boundary applies across all layers, ensuring that the system only works with publicly available information while still providing useful, evidence-based marketing intelligence. [2:40](https://unifapi.podhood.com/673232b3-412f-4256-8ecb-9c8b40e5247a?t=160000) ## Key moments - **[0:00] Four Layers** - **[0:18] Example** - [0:18] A single AI assistant request for Instagram creators triggers four distinct layers—API, MCP, skill, and agent—working beneath the surface to return live public evidence. - **[0:42] API** - **[1:14] MCP** - **[1:47] Skill** - [1:47] A skill is a repeatable playbook that defines evidence collection, comparison criteria, and output format for a research task, without being the data service or connection. - **[2:20] Agent** - [2:40] UnifAPI read-only boundary: 'It can analyze public data and draft recommendations, but it does not post, send DMs, or reach into private social accounts.' - **[2:56] Stack Fit** - **[3:26] UnifAPI Offerings** - **[3:58] Choose Layer** - [3:58] Q: Which UnifAPI layer should I start with for my public-data workflow? - **[4:20] Start Here** ## Topics Agents, MCP, Skills ## Mentioned UnifAPI (company), ChatGPT (product), Claude (product), Codex (product), Instagram (product) ## Transcript ### Four Layers **Host** [0:01] Agent. Skill. MCP. API. If those sound like four names for the same thing, this is the map you need: they are four different layers, and each one owns a different job. ### Example **Host** [0:18] Imagine asking Claude to find Instagram creators for a product launch. You see one request and one answer; underneath, four things happen. A service returns public records. A connector makes those records callable. A playbook turns them into a repeatable research process. And a role decides what to do next. ### API **Host** [0:42] Start at the bottom: an API is the contract with a service. It tells software what you can request, which inputs are required, and what shape comes back. For Instagram research, that might mean a public profile, recent posts, reels, or comments. The API does not decide whether a creator fits your campaign; it reliably returns the records that let someone make that decision. ### MCP **Host** [1:14] MCP sits above that contract, as a connection path for an assistant. Instead of hard-coding a custom integration for every task, Claude, ChatGPT, Codex, or another compatible client can connect to an MCP server, discover available operations, inspect what one operation needs, and call it. MCP does not contain the marketing strategy; it makes capabilities available in a standard, assistant-friendly way. ### Skill **Host** [1:47] A skill is the playbook. It defines one concrete job: what to ask the user, which evidence to collect, which calls to run, how to compare the results, and what the final output should contain. A creator shortlist skill might set audience-fit criteria, inspect recent content, flag brand safety risks, and return a ranked brief with evidence. The skill guides the work; it is not the data service, and it is not the connection. ### Agent **Host** [2:20] An agent is the user-facing role and decision loop. You run the SEO agent, the AI visibility agent, or the Instagram agent inside the assistant you already use. The agent understands the outcome, selects theright skills, asks useful follow-up questions, interprets the evidence, and suggests the next check. In UnifAPI today, that work is read-only research: it can analyze public data and draft recommendations, but it does not post, send DMs, or reach into private social accounts. ### Stack Fit **Host** [2:56] Now the whole stack fits together: the agent decides, the skill guides, MCP connects, the API returns records. A request can move down through those layers, gather live public evidence, then move back up as a useful answer with citations and next steps. Remove any one layer and something changes: less judgment, less repeatability, less connectivity, or no data at all. ### UnifAPI Offerings **Host** [3:26] UnifAPI provides all four product surfaces without pretending they are the same thing. Browse role and platform. Agents by the outcome you need. Load task-specific Skills for repeatable workflows. Connect one MCP server with UnifAPI authorization. Or use the public-data API catalog directly when you are building your own integration. The same public-data boundary stays intact at every layer. ### Choose Layer **Host** [3:58] So, which page should you open? If you want a business outcome, start with an agent. If you want a reusable workflow, choose a skill. If you need to connect your assistant, use MCP. If you are writing the integration yourself, use the API: Outcome, Playbook, Connection, Contract. ### Start Here **Host** [4:20] Start with the outcome at unifapi.com/agents. The Skills, MCP Connection, and public-data API layer are ready when you need them. --- This library is powered by PodHood (https://podhood.com), the podcast website platform.