Coupler.io MCP Server

One MCP for all your business data

Stop wiring up one MCP for Google Ads, another for Meta Ads, a third for Search Console and a fourth for GA4. Coupler.io puts 400+ sources behind a single connection and hands your AI data that is already joined, cleaned and documented, so it answers with your numbers instead of stitching raw API responses together.

Why one data layer beats a stack of single-source MCPs

Ask "Which channel brought us the cheapest customers last month?" With single-source MCPs, the AI does the data engineering in the chat: call three APIs, reconcile spend, cost and amount_spent, line up dates and currencies, then join it all in its context window. Coupler.io does that work once, before the question is asked.

	Single-source MCPs	Coupler.io MCP Server
Sources	One platform per server	400+ sources, one connection
Setup	Install, authorize and maintain each server; many are self-hosted	One sign-in
Cross-channel questions	The AI joins API results in the chat	Sources are joined into one dataset before you ask
Large data	Raw API responses compete for the context window	SQL runs on Coupler.io; only the result comes back
Your definitions	Each platform's own metric names and logic	Your metrics, targets and known data gaps, stored once
Consistency	Depends on how the AI stitches the data each time	The same validated numbers in every chat and every AI tool
Beyond the chat	Chat only	The same data feeds Looker Studio, Sheets, Power BI and dashboards

What you get

Cross-channel datasets, ready to query. Join or append sources with no-code transformations or SQL: ad spend next to Shopify revenue, Search Console queries next to GA4 conversions, campaigns next to closed CRM deals.

A context layer your AI reads first. Data context explains each column: type, units and your name for the metric. Business context explains how the numbers are built: deduplication rules, tracking gaps, your launch calendar and targets. Skills turn recurring analyses into repeatable procedures. Context lives with the data, so every chat in every connected AI tool starts informed.

Build reporting from the chat. Ask your AI to create a data flow, add a source, set a schedule, build a SQL dataset or refresh the data. It confirms before changing anything you didn't ask for.

One reporting automation layer. The data flow that answers questions in Claude also feeds a client dashboard in Looker Studio, a Google Sheet or a warehouse, on schedule. Start from 220+ dashboard templates.

AI inside Coupler.io too. The built-in AI Agent reaches every dataset in your workspace, and AI Insights flags changes you didn't think to look for.

Ask questions that span your whole stack

• "Compare cost per purchase across Google Ads, Meta Ads and TikTok Ads for the last 30 days, using Shopify orders as the source of truth."
• "Which Search Console queries bring GA4 sessions that convert, and which pages rank but don't sell?"
• "What's our CAC across all paid channels this quarter vs last quarter? I need it for the board."
• "Build this week's report for each client: spend, conversions and ROAS against target. Flag anything more than 20% off."

"Our team no longer has to rely on data analysts to get the insights we need. We can just ask questions like 'What's our CAC from Google Ads this month?' and get immediate answers."

Elen Tsarova, Head of Marketing, TitanApps

How it works

1. Create a data flow in Coupler.io, add your sources, and join them if you want cross-channel answers.
2. Pick your AI tool as the destination, run the flow, and schedule it hourly or daily.
3. Connect the MCP server and start asking. Add business context once; every chat uses it.

Tools

Find data

• list-datasets: list the datasets across your data flows
• search-datasets: find a dataset by name, source or keyword
• list-templates: browse ready-made data flow and dashboard templates

Read and analyze

• get-schema: read a dataset's columns, types, descriptions and business context
• get-data: run a read-only SQL query against a dataset on Coupler.io

Shape and combine

• create-dataset: build a new SQL dataset, such as a join, aggregation or pivot
• update-dataset-query: change the SQL behind an existing SQL dataset
• update-dataset-schema: improve column labels and descriptions

Build data flows

• create-dataflow: create an empty data flow
• create-dataflow-from-template: create a preconfigured data flow from a template
• get-dataflow: show a data flow's sources, destinations and last run
• create-dataflow-source: add a source to a data flow
• update-dataflow-source: change a data flow's source
• create-dataflow-destination: add a destination to a data flow
• update-dataflow-destination: change a data flow's destination
• update-dataflow: rename a data flow, or change or pause its schedule

Connect services

• list-credentials: check which services are connected and get a link to connect more
• list-integrations: list the 400+ supported sources and destinations
• get-integration: read what an integration needs and how it authenticates
• get-integration-field-options: fill account-specific choices, such as an ad account or spreadsheet

Remember and refresh

• update-dataset: save business context so future chats start informed
• run-dataflow: refresh a data flow from its sources

Skills

• list-skills: list the reusable analysis skills available to your workspace
• get-skill: load a skill's step-by-step procedure

Security and data access

• Your source credentials stay in Coupler.io. The AI tool never holds your Google Ads, Meta or CRM tokens.
• Data queries are read-only: the AI cannot edit, delete or overwrite imported rows.
• Each AI tool sees only the datasets from data flows sent to it.
• Coupler.io is SOC 2 and GDPR compliant, with published DPA and DORA documentation.