Use the documented client configuration and browser authorization flow, then verify the available tool list.
Compare
Compare website analytics workflows for the AI era.
Compare the tracking, reporting, privacy, migration, and AI-assistant workflows that matter to your team. Start with a concrete evaluation instead of treating any analytics tool as a universal fit.
RequirementEvidence to collectValidation step
Reports and dimensionsCurrent report inventoryReproduce representative reports
Events and funnelsEvent names, payloads, and goalsRun a test conversion path
Privacy and consentFields, hosting, retention, processorsObtain privacy or legal approval
MigrationHistorical exports and parallel dataCompare totals before removal
AI or MCP accessSupported client and required scopesTest read-only queries first
OperationsBackups, upgrades, support, incidentsAssign service ownership
Evaluate Amami
Verify the workflow against your requirements.
The MCP server starts read-only. Write and administrator tools require explicit configuration and remain subject to API permissions.
Ask for metrics, filters, and date ranges behind each answer. Review suggested actions as hypotheses, not guaranteed outcomes.
Compare managed cloud with an Amami-compatible self-hosted deployment, including its security, backup, upgrade, and staffing costs.
Switching from Google Analytics?
Inventory required data, validate representative reports with parallel collection, and decide how much history must remain accessible.
Give your AI assistant an analytics workflow it can use.
Connect a supported client, start with read-only queries, and verify the evidence returned for your real analytics requirements.