What MCP is for
Connecting AI agents to tools, files, databases, APIs, and internal systems through governed interfaces. Use it when the job is specific enough to test against a real workflow, not as a generic platform purchase.
- tool connectors
- agent integrations
- internal APIs
- workflow access
How to use MCP
Start with one repeatable task, one owner, and one success measure. The useful test is whether MCP improves a workflow the team already performs.
- Name the workflow, input, expected output, and human approval point.
- Run a small pilot with MCP using non-sensitive or approved data first.
- Compare output quality, time saved, error rate, and support burden against the manual baseline.
- Write the operating rule before adding more users, more data, or automation permissions.
Implementation workflow
MCP belongs in the stack only when it has a clear place in the work sequence.
- Stage fit: Build, Automate, Govern.
- Primary users: engineers, technical founders, automation builders.
- Deployment model: Protocol and connector ecosystem.
- Pricing check: Open protocol; implementation costs depend on the tools and hosting.
Governance checklist
Before MCP touches production work, make the operating boundary visible to the team.
- Classify the data allowed in the tool and the data that must stay out.
- Limit credentials, connectors, and automation permissions to the pilot workflow.
- Keep a review queue for important outputs and actions.
- Log the decision, owner, cost expectation, and rollback path.
When to use another option
Do not keep MCP just because it is capable. Use another option when the workflow is better served by lower-risk tooling, existing systems, or a simpler manual process.
- permissions must be designed carefully
- unsafe connectors can expose sensitive systems
- Choose a different tool when the team cannot name the owner, review point, or success measure.
