Customized Analytics MCP & APIs
We connect Claude, ChatGPT, and other MCP-compatible AI clients directly to your real analytics data, GA4, Looker Studio, your CRM, so your team can ask a question in plain language and get a real, live answer instead of exporting a spreadsheet.
See sample deliverables →An Analytics MCP server is a connection that lets an AI client like Claude or ChatGPT query your real analytics data directly, using Google’s official GA4 and Looker Studio MCP servers or a custom-built API, instead of you exporting reports by hand. Access is scoped read-only by default, and every connection is documented so your team can maintain it after handoff.
Why teams ask an AI client instead of pulling another report.
The gap is not data, it is the ten minutes it takes to open five tabs, filter, and export before someone can answer a simple question.
Official Google GA4 + Looker Studio MCP integrations used
Default scope; write access is never silently granted
GA4, Looker Studio, CRM, or a custom data warehouse
Every connector shipped with a runbook your team keeps
Query speed and available fields depend on your GA4 property configuration and the MCP client you connect.
What ships when you engage the MCP & API build.
One-time implementation. You own every credential and every connector when we hand it off.
One-time build. No ongoing management fee unless you add a maintenance retainer separately.
A connection your team can query, and maintain.
Six concrete deliverables ship on every Analytics MCP & API engagement.
GA4 and Looker Studio official Google MCP servers connected and authenticated against your properties.
Purpose-built REST endpoints for any data source without an existing MCP server, CRM, warehouse, or internal tool.
Every connection scoped to least-privilege, read-only by default, with write access flagged and never silently granted.
A tested set of real queries run against the live connection to confirm answers match your source-of-truth reports.
Verified working against the specific AI client your team actually uses, Claude, ChatGPT, or another MCP-compatible tool.
Written documentation covering credentials, scope, and maintenance so your team owns the connection after handoff.
We scope the access. Then we build the connection.
A rushed MCP setup either over-grants access or breaks the first time your GA4 schema changes. We scope first, then build to hold.
Which data, at what access level, for which AI client.
Before any server is configured, we map exactly which data sources your team actually needs to query, and at what access level, before touching a single credential.
- 1Data source inventory: GA4, Looker Studio, CRM, warehouse, or other systems.
- 2Query pattern mapping: what questions your team will actually ask.
- 3Access scoping: read-only by default, write access flagged explicitly.
- 4Client compatibility check: Claude, ChatGPT, or your specific MCP tool.
- 5Delivered as a written scope document before any build work starts.
Our team configures, tests, and hands off the working connection.
Once the scope is signed off, we configure the MCP servers or build the custom API, test it against real queries, and document it for your team.
- 1GA4 + Looker Studio MCP servers configured and authenticated.
- 2Custom API endpoints built for any data source without native MCP support.
- 3Credentials scoped to least-privilege before the connection goes live.
- 4Query testing suite run to confirm answers match source-of-truth reports.
- 5Written runbook handed to a named owner on your team.
Bundling multiple services? Request a custom proposal →
Three guarantees on every MCP & API build.
If we miss any of them, we fix it at no additional charge. Written into every implementation agreement.
Every API key and MCP server connection is created under your accounts, not ours. If you ever stop working with MV3, the connection stays live and yours.
Every connection ships read-only unless you explicitly request write access, and any write-access request is flagged and confirmed with you before it is built, never assumed.
A written runbook covering every credential, scope, and connector ships at handoff, so the connection does not depend on any one person remembering how it was built.
What a real MCP & API build actually looks like.
A sanitized view of the kind of connector checklist your team receives at handoff.
MCP & API connector status
Every connector is tracked to completion and tested before handoff, not marked done because a config file exists.
- GA4 MCP server authenticated and query-tested
- Looker Studio MCP server authenticated and query-tested
- Custom revenue API endpoint built and load-tested
- Credential scoping reviewed for least-privilege
- Query test suite run against source-of-truth reports
How we build every MCP & API connection.
Five stages. Same protocol every engagement.
Inventory of your current data sources, existing API access, and what your team actually needs to query.
Written scope document: which sources, which access level, which AI client, signed off before build starts.
MCP servers configured or custom API endpoints built against the signed scope.
Real queries run against the live connection and checked against source-of-truth reports.
Written runbook delivered to a named owner on your team, covering credentials and maintenance.
What teams report after connecting an AI client to real data.
Illustrative composites built from real engagement patterns, not verified individual clients. Used to make the working style concrete, not to claim a specific outcome for a specific company.
Our team used to wait on the analyst to pull a GA4 export. Now anyone can ask Claude directly and get the same numbers our dashboard shows, scoped so nobody can accidentally change anything.
We had a custom warehouse with no MCP server available. MV3 built a scoped API endpoint instead, same workflow, read-only, documented, and our engineering team signed off on the access model.
The handoff runbook mattered more than the build itself. When our analyst left, the next hire didn’t have to reverse-engineer anything, it was already written down.
Illustrative composite personas. Not verified statements from a named, specific client.
One-time implementation fee. No ad spend, no ongoing management fee unless you add a maintenance retainer separately.
- GA4 + Looker Studio MCP servers configured and tested
- Custom API endpoints for any additional data source
- Credentials scoped to least-privilege, read-only by default
- Query testing suite validated against source-of-truth reports
- Written runbook handed to a named owner on your team
- Compatibility confirmed for your specific AI client
Free scoping call. Written implementation quote inside 3 business days. You own every credential at handoff.
This is not for you if…
We hold a fit bar on this service. If any of the below is true, we'll say so on the call.
- You want a generic dashboard, not an AI-queryable connection. That is our AI Dashboards service. This page is specifically about connecting an AI client to query your data directly.
- Nobody on your team currently uses Claude, ChatGPT, or another MCP-compatible tool. There is nothing yet to connect. We recommend starting there before this build.
- You want an AI client to have write access into your analytics or CRM platform. We build read-scoped by default. Write-access requests get flagged and confirmed explicitly, never silently built.
- Your GA4 or CRM setup is not stable yet. If tracking is broken or data is unreliable, we recommend our Analytics Setup service first so there is something worth connecting to.
- Nobody on your team will own the credentials after handoff. We require a named owner who keeps the runbook and can grant or revoke access going forward.
If you’re a fit, keep scrolling. Or book the call now →
Questions buyers ask us.
Leads MV3’s analytics and attribution implementations, including GA4 server-side tracking and AI-queryable data connections.
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