MCP, AI agent with skills, AI chat or deterministic engine: a comparison

MCP, skills, chat and a rules engine often get lumped together as "AI". Each layer answers a different question: how the model gets data, how it knows what to do, who talks to the user, and who actually decides.

Below we compare them on the points that matter most in advertising: where the decision comes from, whether it is repeatable, whether anyone checks measurement, and what protects the budget from mistakes.

How each technology works

MCP: access to data

An MCP server is an adapter between an AI model and a system such as Google Ads. The model can ask about campaigns or run a tool. The official Google Ads server exposes three read-only tools, including GAQL queries. Meta is testing connectors that can edit campaigns. But MCP does not know whether a campaign is in its learning phase or whether measurement works. That knowledge has to come from whatever uses the data.

Agent with skills: instructions for the model

A skill is a folder with an instruction and optional scripts. The model loads it when it decides it matches the request. A good skill can carry advertising knowledge, but the model still interprets it probabilistically. The Agent Skills documentation says it plainly: reliability needs code, and a malicious skill can direct the model to act against its description.

AI chat: a conversation on the data you provide

A chat is very good at explaining, summarizing and helping you think. But it works only on what you paste or what fits in its context. It has no account history from recent months, does not know that conversions from the last few days are still arriving, and may answer the same question differently.

Deterministic engine: a decision from a rule and data

Phanes runs hundreds of expert rules on daily metrics collected every night. Each rule has a condition, a threshold, an allowed action and a rationale, and its confidence rises or falls with every measured result. The engine first checks measurement and the learning phase, then proposes a change, validates it in the API and waits for approval. Phanes's own language model, Aether, only turns the result into readable sentences and is penalized in training for any number outside the data.

The comparison in one table

MCP, skills and chat are layers that help a model work. None of them adds advertising knowledge, a measurement check or a safe change path on its own. In Phanes those are built into the engine.

One command, four paths

The command is "Raise the budget of the best-selling campaign". Here is what happens next:

What separates them is not who has the better model but how many control steps stand between the command and a change in the account.

When each one makes sense

Facts worth knowing

• The official Google Ads MCP server (open source, October 2025) has three tools, and all of them are read-only.
• Meta offers AI connectors for ads (an MCP server) in open beta. They can create and edit campaigns, and the owner of a business portfolio can limit what the agent does in the account.
• The MCP specification describes tools as arbitrary code execution and notes that the protocol cannot enforce security principles itself. That is the implementer's job.
• Agent Skills documentation: instructions give flexibility, code gives reliability. A malicious skill can direct the model to use tools in ways that do not match its description.
• Model vendors state it in their documentation: even at temperature 0, results are not fully deterministic.

MCP is a channel to your data, a skill is an instruction and a chat is a conversation. A budget decision should come from an engine that gives the same answer on the same data, and a human should approve it.