MCP and APIs serve different callers. An API gives software a defined way to perform an operation. The Model Context Protocol, or MCP, gives an AI application a standard way to discover and invoke tools described by an MCP server. MCP can sit in front of existing APIs rather than replace them.
That distinction matters when designing an AI workflow. A scheduled process that always updates one CRM field may need only a direct API or app module. An AI application that must choose between looking up an order, creating a task and requesting approval may benefit from a bounded MCP tool interface. The practical choice depends on the caller, the predictability of the operation and whether tool selection must happen at runtime.
This is a decision and design guide, not a report of a live integration test. The Make examples describe documented product capabilities where stated and proposed implementation controls where marked illustrative.
MCP vs API: decide by caller and job
An API is a defined interface selected and implemented by a software integrator. The integrator chooses the operation, handles authentication, sends a request in the expected format and processes the response. APIs can use REST, GraphQL or other interface patterns, but the key characteristic here is that the software calling the interface already knows what operation it wants.
MCP standardizes how an AI application can discover and invoke tools exposed by an MCP server. The AI host selects a described tool, its MCP client sends structured arguments to the server, and the server performs the configured work. That work may call an existing application API. The destination API still governs its own operation.
Choose for the caller and the operation: use a direct interface when the work is known and stable; use MCP when an AI application needs to discover and select described tools at runtime.
What each interface exposes, and what it does not
With a direct API, the integrator chooses a specific endpoint or operation and builds around its documented authentication, request and response behavior. The calling application controls the request shape and usually contains the integration logic needed to handle errors, retries and destination responses.
With MCP, an AI application can discover tools that a server makes available, along with descriptions and input requirements, then call a selected tool with structured arguments. The tool description helps the model understand when a capability applies. It does not authorize a particular user to change a particular customer record.
Keep five concerns separate:
- Tool description: what the capability does and which inputs it accepts.
- Technical access: which client, organization, team, scenario or service principal may invoke it.
- Business validation: whether this record, amount, status or customer may be changed.
- Approval: whether a person must authorize a consequential action.
- Audit: who requested the action, what was selected, what ran and what changed.
MCP uses JSON-RPC-style messages and supports local and remote transport patterns, but transport and session behavior depend on the specification version and implementation. Current protocol materials have evolved, and the current Make reference discusses Streamable HTTP as well as SSE support. Do not assume every MCP server is stateful or uses the same remote transport. Check the relevant implementation documentation before designing around sessions. See the Anthropic MCP overview and the MCP specification change log.
Choose an interface using the operation, not a tool-count rule
There is no vendor-verified universal number of integrations at which MCP becomes worthwhile, and no general evidence that MCP is faster than a direct API call. MCP may reduce duplicated integration work or improve tool discovery in a multi-tool AI workflow, while adding its own discovery, authorization and operational layers.
| Situation | Better starting point | Why |
|---|---|---|
| One known, repeatable operation | Direct API or app module | The caller already knows the operation and request shape. |
| An AI application must choose among tools at runtime | MCP | Described tools provide a standard discovery and invocation boundary. |
| AI proposes an action, but execution must be predictable | Hybrid | MCP can handle bounded selection while deterministic validation and writes remain behind it. |
Known caller, fixed operation
Use a direct API or app module when the workflow already knows what to do. This avoids model-driven selection where it adds no operational value.
AI selection, bounded execution
Expose only relevant capabilities for the AI application to choose from, then keep business rules and consequential writes in deterministic workflow steps or the destination system.
A hybrid design is often practical. Stable operations remain behind APIs or app modules, while a narrow set of suitable capabilities is exposed as AI-facing tools. Make documents both direct API-based automation and MCP-based tool access. Its MCP Server exposes Make scenarios to external MCP clients, while Make AI Agents can connect to external MCP tools subject to the capabilities of the client, server and authentication method in use.
A practical Make example: expose a scenario as an MCP tool
Make documents a scenario-based MCP Server pattern. The scenario must be active and use on-demand scheduling. Configure its inputs and outputs to define the tool interface, and provide a clear description so a client can understand when the tool applies. The scenario’s configured modules determine the work it performs.
Make documents scenario-run scopes for all plans, while broader management scopes are available on paid plans. Make currently advertises MCP access across its plans, but scenario executions consume credits. Included access is not unlimited execution, and Free plan availability does not imply access to every management capability. Check the Make MCP Server setup documentation, the technical reference and current Make plan details before rollout.
For a predetermined operation, the alternative is to call a specific Make scenario through the Make API. The documented API can run an active scenario, accept configured input data and return an execution ID. Authentication and required API scopes still apply. The following request is an illustrative design shape, not a verbatim Make endpoint schema:
{
"scenario_id": "configured scenario identifier",
"data": {
"request_id": "req-illustrative-1042",
"record_id": "CRM-illustrative-582",
"action": "approved update"
},
"responsive": false
}A returned execution ID identifies a scenario run. It does not prove that a business-level change succeeded or prevent duplicate actions. Use the Make API reference for documented responsive or non-responsive execution and available callback or result-retrieval behavior. For teams designing scenario contracts and access scopes, see Make automation consulting.
Keep AI intent separate from business validation
Consider a hypothetical support workflow inspired by Make’s published example involving Zendesk, Shopify and HubSpot. Make presents this as a possible use case, not as a complete reusable template with verified field mappings, approval logic or deduplication controls.
| Trigger | AI job | Validation and action | Fallback |
|---|---|---|---|
| Support ticket requests an order-related update | Extract a proposed field and value from the ticket context | Retrieve the exact ticket and order, resolve the CRM record, check permissions and current values, then write only an approved field | Ambiguous identity or disallowed field goes to a support owner |
| User asks for a project task | Turn free-form text into a bounded title, owner and due date | Resolve the project and owner, validate date and required fields, then create once using a durable request key | Unknown owner, project or duplicate request is held for review |
| Scheduled finance report request | Classify the requested reporting period and summarize retrieved data | Validate period boundaries, source grain, currency and approval status before distribution | Reconciliation mismatch or missing source data blocks email delivery |
The table contains proposed operating designs, not documented Make templates. A safe CRM sequence is:
- Retrieve: use exact ticket and order identifiers to fetch relevant records. Do not ask the model to guess which customer or order matches.
- Extract: have AI return a proposed change inside a bounded structure. The model may interpret language, but it does not grant permission.
- Validate: confirm source and target IDs, caller authorization, allowed fields, status transitions, required values and target freshness.
- Write or escalate: apply the approved change to the exact CRM record, or route ambiguity, stale data and consequential changes to a named owner.
- Record: save the request, source ticket, order, target record, proposed and applied change, outcome and execution ID.
For concurrent writes, a lookup-before-create check is not sufficient. Use a durable uniqueness key such as source_system + source_ticket_id + action_type + target_record_id, enforce it with a database unique constraint or transactional upsert where available, and use a queue or single-writer pattern when the destination cannot provide atomic uniqueness.
The following is a proposed result contract for this example. Its fields are editorial design fields, not Make defaults:
{
"request_id": "req-illustrative-1042",
"source_ticket_id": "ticket-illustrative-77",
"order_id": "order-illustrative-18",
"target_record_id": "CRM-illustrative-582",
"proposed_change": {
"field": "approved field",
"value": "proposed value"
},
"status": "needs_review",
"execution_id": "execution-illustrative-991",
"exception_code": "AMBIGUOUS_RECORD_MATCH"
}- Resolve exact source and target records; send ambiguous matches to a person.
- Allow only specified fields and transitions, with required values checked deterministically.
- Read the target before writing and reject stale or conflicting values.
- Enforce a durable uniqueness key with an atomic constraint or transactional upsert when concurrent runs are possible.
- Name a business owner for approvals, partial completion and unresolved exceptions.
Make’s product page does not establish the exact triggers, field mappings, validation or deduplication for this support example. Treat the workflow as an operational design inspired by a published use case. For teams reviewing CRM record ownership and update controls, see CRM systems consulting.
Plan for timeouts, access boundaries and ownership
A timeout does not necessarily mean that a Make scenario failed. Make documents behavior in which a scenario may continue after an MCP tool call times out. An execution ID may help retrieve results when the applicable mechanism and scope are available.
- Persist the request ID and execution ID, then mark the request as pending rather than failed.
- Check scenario status and destination state through the documented retrieval mechanism.
- Do not issue a second write while the first run is unresolved. Reconcile the destination before retrying.
- Route partial completion to the workflow owner, who decides whether to resume, correct or escalate.
Assign responsibilities explicitly. The system owner manages credentials and access scope. The workflow owner handles scenario failures and reconciliation. The business owner approves consequential changes. Tool visibility is not record-level authorization, and MCP does not automatically provide approval, idempotency, rollback or business validation.
Use least privilege. Expose only intended scenarios and tools, restrict MCP-token access where possible and avoid broad management scopes unless a defined operational need justifies them. Make provides visual visibility into MCP tool runs and scenario execution, but that does not mean every underlying third-party request has identical canvas-level observability.
Measure whether MCP adds value
Pilot one low-risk workflow with a baseline, named owner and acceptance criteria. Track whether the AI application selects the right tool, how often inputs fail validation, completion and exception rates, duplicate writes, human review effort and the work required to maintain a new integration.
Compare equivalent workflows across the full operating cost. Include authentication, schema changes, monitoring, recovery and governance, not only the duration of a tool call. A direct API may remain preferable even when an AI workflow could technically use MCP.
Keep event records at their actual grain. Store one record per tool invocation and one record per Make scenario execution, linked by durable request and execution identifiers. Keep aggregate metrics separate from raw events. If an AI run produces several citations, updates or executions, assign separate identifiers to each observation rather than treating the day or prompt as a unique row.
Decision: retain direct API or app-module calls for predictable work, use MCP when AI-driven discovery adds practical value and keep the AI-facing boundary narrow while deterministic controls govern consequential execution.