Ask an AI agent to pause every campaign with a CPA above target, and two
things have to happen: something has to find those campaigns, and
something has to change their status. The Google Ads API and the Meta
Marketing API have done both reliably for years, for any developer
willing to read the docs and write the code. Model Context Protocol
(MCP) changes who does the reading.
So the useful MCP vs API question is not which one wins. It is what MCP
adds, what it costs, and where each belongs when the action spends
money, answered here with real-world ad platform calls.
MCP vs API: The Short Answer
**An API is how software talks to software. MCP is a standard way for an
AI application to discover and call tools, and an MCP server usually
calls an API underneath. MCP is not a replacement for the API; it adds a
layer the model can use.** The real difference between MCP and APIs is
less about what each can do and more about who the caller is.
Direct API call from MCP in an AI client
code
- ----------------------- ----------------------- -----------------------
Who it is built for Developers writing AI applications whose
integration code model picks actions at
runtime
How capabilities are A person reads the docs tools/list at runtime:
found and hardcodes the calls names, descriptions,
input schemas
Interface format Endpoints, HTTP JSON-RPC 2.0 messages
methods, over stdio or
resource-specific Streamable HTTP
payloads
State Independent requests Stateless in the
current spec: each
request carries its
version and
capabilities
Auth Provider's choice: API Optional; over HTTP,
keys, OAuth 2.0, OAuth 2.1 with the
service accounts server as resource
server
Cost profile Per request; nothing Tool definitions and
enters a model's results cost model
context tokens when a model is
in the loop, on top of
the API calls
Best for Fixed pipelines, Agents choosing
scheduled jobs, actions; one
high-volume sync integration across AI
clients
What Is an API?
An application programming interface is a contract: send a request in
this shape to this address, and you get a response in that shape. A REST
API exposes endpoints over HTTP; you authenticate with an API key or an
OAuth access token, stay inside the provider's rate limits, and handle
the errors it documents. Think of a restaurant menu: you can order only
what is printed, in the words printed, and the kitchen does not care why
you are hungry.
Pulling 14 days of campaign cost and conversions from the Google Ads API
is a [POST of a Google Ads Query Language (GAQL)
query](https://developers.google.com/google-ads/api/rest/common/search):
POST
https://googleads.googleapis.com/v25/customers/1234567890/googleAds:search\
Authorization: Bearer ACCESS_TOKEN\
login-customer-id: 9876543210\
Content-Type: application/json\
\
{\
"query": "SELECT campaign.id, campaign.name, metrics.cost_micros,
metrics.conversions FROM campaign WHERE segments.date DURING
LAST_14_DAYS"\
}
Abridged response with illustrative values:
{\
"results": \[\
{\
"campaign": {\
"resourceName": "customers/1234567890/campaigns/111222333",\
"id": "111222333",\
"name": "Search - Brand"\
},\
"metrics": {\
"costMicros": "1140000000",\
"conversions": 12\
}\
}\
\],\
"fieldMask":
"campaign.id,campaign.name,metrics.costMicros,metrics.conversions"\
}
Each row comes back with cost in micros, where one million micros is one
unit of the account's currency. Every detail in that request, from
header names to query grammar, came from documentation before a line of
code was written. That is the cleanest way to frame MCP vs REST API: **a
REST call assumes the caller already knows the menu.**
What Is MCP (Model Context Protocol)?
**MCP is an open protocol for connecting AI applications, and the AI
agents running inside them, to external tools and data.** Anthropic
open-sourced it in [November
2024](https://www.anthropic.com/news/model-context-protocol), and the
current specification revision is [dated 28 July
2026](https://modelcontextprotocol.io/specification/2026-07-28).
Messages are JSON-RPC 2.0, and a server can offer three kinds of
capabilities: tools the model can execute, resources that supply context
and data, and prompts, which are templated messages a user picks.
The Three Parts: Host, Client, Server
- The host is the AI application. Claude, ChatGPT, Cursor, or your
own agent: the program that coordinates the AI model and asks the user
for consent.
- The client lives inside the host. It is a connector that talks to
exactly one server, so a host connected to five servers runs five
clients.
- The server holds the capabilities. It can be a local process or a
remote service, and for an ad platform it is a bridge to that
platform's API.
Local servers typically talk over stdio, the standard streams of a
subprocess the client launches, and remote servers over Streamable HTTP,
where each message is an HTTP POST to a single endpoint. One recent
change matters: as of the 2026-07-28 revision, [MCP is
stateless](https://modelcontextprotocol.io/specification/2026-07-28/basic/versioning).
Every request carries its own protocol version and the client's
capabilities, and the initialize handshake belongs to legacy revisions.
A server that offers tools still declares the tools capability, which a
client can read up front with server/discover.
How an AI Agent Finds and Calls a Tool
The whole loop is two methods. First, the client asks what exists:
{"jsonrpc": "2.0", "id": 1, "method": "tools/list",
"params": {}}
{\
"jsonrpc": "2.0",\
"id": 1,\
"result": {\
"resultType": "complete",\
"tools": \[\
{\
"name": "pause_campaign",\
"description": "Pause one campaign by ID",\
"inputSchema": {\
"type": "object",\
"properties": {\
"platform": {"type": "string"},\
"campaign_id": {"type": "string"}\
},\
"required": \["platform", "campaign_id"\]\
}\
}\
\]\
}\
}
Then, when the AI model decides the tool fits the request, the client
calls it:
{\
"jsonrpc": "2.0",\
"id": 2,\
"method": "tools/call",\
"params": {\
"name": "pause_campaign",\
"arguments": {"platform": "google", "campaign_id":
"111222333"}\
}\
}
The model does not need an endpoint, header, or credential, only a name,
a sentence of description, and a JSON Schema, and it decides from those
whether the tool matches the request. (Both snippets are simplified and,
like the spec's own examples, omit the required \_meta block.)
What MCP Adds on Top of an API
The MCP vs API difference is easiest to see in the five things the
protocol adds to an API that already works.
Runtime tool discovery. The model asks what the server can do when
it needs to, instead of a developer deciding at build time. The spec
even lets a server return only the tools the caller's [granted scopes
permit](https://modelcontextprotocol.io/specification/2026-07-28/server/tools).
Descriptions and schemas a model can read. Every tool arrives with a
plain-language description and a JSON Schema for its inputs, so
documentation and type checking travel in one payload.
One integration for many AI clients. Meta publishes one ads MCP
server URL, https://mcp.facebook.com/ads, for ChatGPT, Claude, Claude
Code, and Perplexity. Without a shared protocol, that is four
integrations to maintain.
A standard auth flow that keeps credentials away from the model.
Over HTTP, a protected MCP server acts as an [OAuth 2.1 resource
server](https://modelcontextprotocol.io/specification/2026-07-28/basic/authorization)
and can point unauthenticated callers at its authorization metadata. We
sent an unauthenticated tools/list to our own server while writing this
and got exactly that challenge:
HTTP/2 401\
www-authenticate: Bearer error="invalid_token",
resource_metadata="https://mcp.synterai.com/.well-known/oauth-protected-resource",
scope="tools:read tools:write"
The client follows that pointer, sends the user through a browser
sign-in, and keeps the token out of the chat. The model sees tool
definitions and results, never the token.
A place to put guardrails between the model and the API. Because
every action passes through the server, the server can refuse it.
Meta's ads MCP server enforces rules an admin sets, such as blocking
campaign creation or rejecting any budget above USD 1,000. Synter's
server runs a fail-closed allowlist, so a call to an unregistered script
is rejected before it reaches an ad platform.
MCP vs Function Calling
Function calling (Anthropic calls it tool use) is a feature of each
model provider's API, in that provider's format: you pass tool
definitions with the request, and the model returns a structured call
for your code to run. MCP sits a level above, as a cross-client standard
for where those tools come from, and the two meet under the hood.
[OpenAI's API, for
example](https://developers.openai.com/api/docs/guides/tools-connectors-mcp),
lists a remote MCP server's tools and offers them to the model next to
the functions you wrote yourself. Function calling is how a model asks
for an action; MCP is how a host learns which actions exist and who
serves them.
What MCP Costs You (and When a Direct API Call Wins)
Every tool a model can call has to be described to it, and descriptions
are tokens. [Anthropic's tool-use
docs](https://platform.claude.com/docs/en/agents-and-tools/tool-use/overview)
list the sources: tool names, descriptions, and schemas in the request,
plus every tool call and every tool result. **In ad work, the results
are usually the expensive part,** because a 14-day campaign report
becomes context the model has to read rather than a file your code
parses.
Three other costs show up once AI agents do real work.
An extra hop. The client calls the server, the server calls the
platform API, and the answer travels back through both, which adds
latency compared with your code calling a traditional API directly.
Non-deterministic selection. The model chooses the tool and its
arguments. That is the point of an open-ended question and a liability
in a fixed pipeline that should run the same way every morning.
Large API surfaces eat context. Spotify's engineering team chose a
CLI backed by an OpenAPI spec for its Ads API and "deliberately avoided
MCP for now," because defining every endpoint as a tool would consume
significant context in every interaction ([Spotify
Engineering](https://engineering.atspotify.com/2026/5/spotify-ads-api-claude-plugins)).
MCP vs API vs CLI
A command-line tool is the third option, and for coding agents it is
often the cheapest. [Claude Code's cost
guidance](https://code.claude.com/docs/en/costs) says CLIs
such as gh and gcloud are more context-efficient than MCP servers
because they add no per-tool listing. It also notes that Claude Code
defers MCP tool definitions by default, so only names enter context
until a tool is used. Clients keep getting better at hiding the
overhead, so compare per workflow, not per protocol.
Worked Example: The Same Ad Task Through an API and Through MCP
Take one task: an advertiser with Google Ads and Meta accounts, both in
USD, wants every campaign whose CPA over the last 14 complete days is
above an \$80 target paused, done three ways. Assume one purchase
conversion is counted the same way on both, and skip campaigns with zero
conversions.
Direct API: What a Developer Has to Build
On Google Ads:
- OAuth 2.0 credentials with the AdWords scope from a Google Cloud
project with an approved API access level, plus a login-customer-id
header when you work through a manager account. Google sunset
developer tokens on 9 September 2026 and now ignores them ([Google
Ads API
docs](https://developers.google.com/google-ads/api/docs/get-started/dev-token)).
- The GAQL query above, then your own arithmetic: cost micros divided
by a million, divided by conversions, compared with \$80, skipping
campaigns with zero conversions.
- A
update that sets status to PAUSED, with an updateMask naming that
field.
On Meta:
- A Meta app with ads_read to report and ads_management to change
anything.
- An Insights call with level=campaign and date_preset=last_14d,
reading cost_per_action_type for your conversion event.
- A POST to each /{campaign_id} with status=PAUSED, which also pauses
its active ad sets and ads.
That is two auth models, two query styles, and two error formats, all
owned by you. In exchange, **the job runs identically every time, costs
no model tokens, and can run at 6 a.m. without anyone watching.**
An Official Single-Platform MCP Server
Platform-owned servers remove most of that build, but not with the same
permissions.
Google's Ads MCP server is read-only. Google's guide says it
"cannot modify bids, pause campaigns, or create new assets" ([Google
Ads API
docs](https://developers.google.com/google-ads/api/docs/developer-toolkit/mcp-server)).
An agent can run the CPA query through its search tool; the pause still
needs a person or your own API code.
Meta's ads MCP server reads and writes. [Meta announced it in open
beta on 29 April
2026](https://www.facebook.com/business/news/meta-ads-ai-connectors),
and it can create and edit campaigns, ad sets, and ads. The status
change can happen in the same conversation, subject to the account's
MCP server rules and your authorization ([Meta Business Help
Centre](https://www.facebook.com/business/help/1456422242197840)).
Snap's Ads MCP server is read-only in its current release ([Snap
developer
docs](https://developers.snap.com/marketing-api/Ads-MCP/Introduction)),
which matters once the task grows past Google and Meta.
The model now discovers and reads without your code, but you run two
servers with two permission models, and the CPA comparison still happens
in the model's context. Official agent access has [arrived in very
different
shapes](https://syntermedia.ai/blog/official-ad-platform-agent-access).
One MCP Server in Front of Many APIs
This is the layout Synter uses, one option among several (a team can
build this layer itself). The agent connects to one endpoint,
https://mcp.synterai.com, and Synter calls each ad platform's API
directly behind it. Our [tool
reference](https://docs.syntermedia.ai/mcp/tools) lists 141
tools, each marked read-only or write. Here the agent reads with
pull_google_ads_performance and pull_meta_ads_performance (date_range
set to last_14_days, on the named accounts), compares CPA, then calls
pause_campaign once per campaign with platform set to google or meta.
The difference is where the controls sit. In review-required mode, a
person approves before a launch or edit goes out, hard caps are checked
before any platform API call, and recent changes can be rolled back. The
trade is a vendor between your agent and your ad accounts, which
first-party servers do not ask of you.
Step Direct API Platform MCP One MCP over many
APIs
- ----------------- ------------------ ----------------- -----------------
Auth setup OAuth per Sign-in per One MCP sign-in;
platform, in your server in the AI ad accounts
code client connected once in
the vendor's app
Find high-CPA GAQL query and Read tools on Read tools on one
campaigns Insights call, each server, server, compared
math in code compared in in context
context
Pause campaigns:mutate Meta: write tool, pause_campaign on
and POST subject to admin both platforms
/{campaign_id} rules. Google:
not available
Guardrails Whatever you build Each platform's One approval mode
own model and cap set
Model token cost None Definitions and Definitions and
results from two results from one
servers server
Best when Scheduled jobs One platform, Cross-platform
with fixed logic first-party questions, one
access set of controls
When to Use MCP vs When to Call the API Directly
Deciding when to use MCP vs API comes down to two questions: who is
making the call, and how often the logic changes.
Call the API directly when:
- The job is fixed: a nightly spend export, a warehouse pipeline, a
scheduled ETL run.
- You need the same result every run and a code review on every logic
change.
- Volume is high enough that model tokens would dominate the bill.
- Latency matters more than flexibility.
Use MCP when:
- A multi-step task needs a model to choose actions based on what it
finds.
- You want one integration for Claude, ChatGPT, Cursor, and whatever
client ships next.
- People who do not write code need to ask questions of the account.
- You want approvals and spend caps between the model and the API, not
inside each script.
- You are prototyping a workflow before deciding whether it deserves a
pipeline.
Use both, which is the common pattern. Traditional APIs stay
underneath, and MCP is the AI-facing layer. Synter exposes the same
tools both ways: a REST endpoint for code that runs on a schedule and
the MCP server for agents, and one API key can cover both. Where to draw
the protocol boundary is a design decision, and our post on [why we
call ad platform APIs directly behind our MCP
server](https://syntermedia.ai/blog/why-we-dont-use-ad-platform-mcps)
explains ours. The working rule: **if an action can spend money, enforce
approvals and caps in the layer every entry point passes through**, MCP
and REST alike, so no path skips them.
To try the worked example on your own accounts, [connect Synter's
hosted MCP server](https://syntermedia.ai/mcp) to Claude or
Cursor (code MCP100 on that page adds \$100 in ad credit) and start with
a read, such as 14-day CPA by campaign, before you let anything pause.
MCP vs API Security in One Paragraph
An API key in a script and an OAuth token held by an MCP client protect
the same accounts, but MCP adds a new kind of attacker: text. An agent
can act on instructions hidden in tool outputs, a technique known as
prompt injection, which is why Snap's docs advise treating any MCP
server like any other third-party integration. The spec's defenses are
concrete. Servers must accept only tokens issued for them and must not
pass them through to the downstream API. Hosts must get user consent
before invoking a tool, and hints such as readOnlyHint count as
untrusted unless the server is trusted. In practice, that means
least-privilege scopes, approval gates on anything that spends, and a
change log you can audit afterward.
FAQ
Will MCP replace APIs?
No. MCP servers are usually a layer over existing APIs: Google describes
its Ads MCP server as a bridge to the Google Ads API, and Meta describes
an MCP server as a bridge to systems such as databases or APIs. Take the
API away, and an ad platform's MCP server has nothing to call.
Is MCP slower than API?
Usually, for a single action. A server that wraps an API adds a network
hop, and in an AI client the model also reads tool definitions and
results before it acts. For multi-step work, time the whole workflow,
not one call.
Can an API be an MCP server?
Not as-is, because a REST API does not answer tools/list or tools/call.
The MCP server vs API distinction is one of roles: the MCP server speaks
JSON-RPC to the client and translates tool calls into API requests. It
can run as a separate process or as an endpoint the vendor hosts next to
its API. (ChatGPT is an app rather than an API, and it can act as an MCP
host.)
Is MCP just JSON-RPC?
JSON-RPC 2.0 is the message format. MCP is the meaning on top: which
methods exist, how tools describe their inputs, how version and
capabilities travel with each request, and how authorization works over
HTTP.
Is MCP the same as function calling?
No. Function calling lets a model emit a structured call; MCP
standardizes where those tools come from.
Do I need MCP to connect Claude to my ad accounts?
You need some tool layer, and for Claude that is usually a remote MCP
server added as a custom connector. Platform servers cover one platform
each; a cross-platform server such as Synter's covers several.