MCP vs REST API for AI agents: choose the workflow boundary, not the fashionable protocol
“Should we use MCP or a REST API?” is usually the wrong first question.
The better question is: what boundary are you trying to give the agent?
REST and MCP can both move data. They shape agent behavior in very different ways.
REST is a product API boundary
REST works well when the contract is stable, resource-oriented, and already part of your product architecture. It is familiar, cacheable, well-supported, and easy for non-agent clients to consume.
If you are exposing a public integration, REST may still be the right interface.
Related: Connect ChatGPT to a SQL database safely.
MCP is an agent tool boundary
MCP is useful when the client is an AI agent that needs discoverable tools, descriptions, parameters, permission context, and structured errors. The interface is not just “call this endpoint.” It is “choose the right tool for this task.”
That distinction matters when the workflow touches live databases, internal APIs, or operational systems.
Related: MCP server setup.
Use MCP when policy must travel with the tool
For database workflows, the tool should carry boundaries: read-only access, approved views, tenant scope, row limits, and audit behavior. Those constraints help the agent choose a safe path before any query runs.
A plain API can enforce policy too, but MCP makes the tool contract visible to the AI client.
Related: MCP database tool allowlists.
Use REST when humans and systems need the same contract
If the same endpoint serves dashboards, partner integrations, mobile apps, and agents, REST may be the cleaner shared contract. You can still wrap selected REST actions in MCP tools when the agent needs a task-oriented interface.
The answer is often both: REST behind the service boundary, MCP at the agent boundary.
Watch the audit trail
With agents, teams need to inspect intent, tool choice, policy decision, source, result size, and final answer. MCP workflows should make that chain observable.
Related: Audit-ready MCP database workflows.
Where Conexor fits
Conexor is MCP infrastructure for teams that want AI clients to query databases and APIs through controlled tool boundaries instead of handing models broad, ambiguous access.