Your apps. One useful plan for your agent.
Choose your business apps and get an MCP connection plan, matched SQL tutorials and a ready-to-copy agent prompt. Free Markdown and JSON downloads; no signup needed.
Your plan · 3 apps
Which useful analyses can I run across these business apps?
Choose a pair example
HubSpot + Stripe
HubSpot pipeline and Stripe paid invoices
- Reporting unit
- One row per CRM company and currency
- Identity map
- Map each scoped Stripe customer to its HubSpot company; check duplicate and unmatched customer mappings.
Calculation checks for this example
- Choose current open-deal stages and an exact invoice-payment interval.
- Normalize source currency units, then aggregate deals and qualifying invoices separately.
- Keep companies without matched invoices; distinguish a complete zero from missing billing coverage.
HubSpot + Intercom
HubSpot deals and unresolved Intercom conversations
- Reporting unit
- One row per CRM company; separate currencies for any monetary measure
- Identity map
- Resolve each conversation to its intended reporting company through explicit linkage or a reviewed bridge. Do not assign every contact-associated company to every conversation; leave ambiguous matches for review.
Calculation checks for this example
- Choose the customer-message interval and currently open conversations; internal updates and closed or snoozed conversations do not qualify for this example.
- Count distinct conversations, not messages, participants or joined deals.
- One company can have several conversations and deals; aggregate each record set first.
Records to connect
Supplies a matched analysis
Reference record types: contacts, companies, deals, campaigns, companies_property_history, contact_lists
Supplies a matched analysis
Reference record types: customers, invoices, charges, subscriptions, refunds, events
Supplies a matched analysis
Reference record types: conversations, contacts, conversation_parts, teams, companies, activity_logs
Setup, dataset coverage and decisions before running
These are public dataset hints, not guaranteed table names or a complete inventory. The planner connects no accounts and runs no API calls or customer-data queries. Discover your actual granted datasets after setup.
These are separate pair examples. No combined multi-app SQL fixture has been tested here.
Connect and check coverage
- In Combined Connected Apps, check each source's current Ready status and setup form. Authorize there, choose datasets and complete the first successful sync.
- Grant the agent these sources explicitly in Access or Set up. A new source does not automatically enter an ordinary grant.
- Discover the actual source and dataset schemas. The public catalog lists reference names, not guaranteed live tables or complete field lists.
- Validate each identity map, duplicate keys and unmatched records. Give every calculation a clear reporting unit before combining results.
- Check successful commits, available date coverage and the freshness needed for the decision. Missing coverage is unknown, not zero.
- Agree date boundaries, states, measure definitions and currency units. Compute on all eligible records, then display a bounded answer with supporting evidence.
Decisions for your data
- Exact reporting interval and observation date, including the timezone.
- Source accounts, live/test scope, required datasets and an acceptable freshness threshold.
- Maintained identity mappings and the treatment of duplicate or unmatched records.
- These are separate tested pair examples. Select one analysis first, or agree a shared reporting unit before composing them; no combined multi-app SQL fixture has been tested here.
Source references
- HubSpot source documentation · reference version 6.8.0
- Stripe source documentation · reference version 6.0.13
- Intercom source documentation · reference version 0.13.24
Planning reference: 2026-09-10. Example records are synthetic; your connection determines actual coverage and freshness.
Read the agent assignment
Use my authorized Combined MCP connection to plan this analysis: Which useful analyses can I run across these business apps?
Selected apps: HubSpot, Stripe, Intercom.
Use list_sources and list_datasets, following pagination on both, then describe_dataset to find the actual granted sources, logical dataset names and fields. Do not invent table names from the catalog.
Use get_freshness for the required sources, in batches of at most eight source IDs. An app can resolve to several Sources. Report any missing successful commit or date coverage before calculating.
Analysis: HubSpot pipeline and Stripe paid invoices.
Reporting unit: One row per CRM company and currency.
Map each scoped Stripe customer to its HubSpot company; check duplicate and unmatched customer mappings.
Choose current open-deal stages and an exact invoice-payment interval.
Normalize source currency units, then aggregate deals and qualifying invoices separately.
Keep companies without matched invoices; distinguish a complete zero from missing billing coverage.
Analysis: HubSpot deals and unresolved Intercom conversations.
Reporting unit: One row per CRM company; separate currencies for any monetary measure.
Resolve each conversation to its intended reporting company through explicit linkage or a reviewed bridge. Do not assign every contact-associated company to every conversation; leave ambiguous matches for review.
Choose the customer-message interval and currently open conversations; internal updates and closed or snoozed conversations do not qualify for this example.
Count distinct conversations, not messages, participants or joined deals.
One company can have several conversations and deals; aggregate each record set first.
Resolve these decisions before execution:
- Exact reporting interval and observation date, including the timezone.
- Source accounts, live/test scope, required datasets and an acceptable freshness threshold.
- Maintained identity mappings and the treatment of duplicate or unmatched records.
- These are separate tested pair examples. Select one analysis first, or agree a shared reporting unit before composing them; no combined multi-app SQL fixture has been tested here.
For a composed analysis, aggregate each independent record set before joining; never raw-join every selected app. Treat unmatched data separately from a verified empty result. Only add extra sources for an explicit context need.
Use read-only query_sql with discovered logical relations and parameterized values. Keep SQL at most 16 KiB and at most 100 parameters. Calculate over the complete eligible dataset before limiting the displayed answer to 50 rows (the tool limit is 1,000).
Return the decision table, exact definitions and time bounds, source freshness, mapping exceptions, supporting record references and query receipt when supplied. If literal text context is needed, use bounded search_context and state its coverage.
Return proposed follow-up actions for review; this plan does not authorize sending messages, changing records or adding source grants.Free to use. No credentials, business-data upload or account connection needed here.
Turn a stack of business apps into questions your AI agent can answer. Pick two to four sources, choose an analysis, and get the records, customer mappings and query steps to bring it together. Save the plan, share your selection, or jump into a runnable example.
Start with the answer you want
Your CRM knows the deal. Billing knows the payment. Support knows what is blocking the customer. Combined brings those sources into one queryable layer for your agent. This planner gives you a practical starting point: which records to connect, how to recognize the same customer across apps, and what to calculate.
With HubSpot, Stripe and Intercom selected, start with two useful questions: how open pipeline compares with paid invoices, and which CRM companies have unresolved support conversations. Each has its own worked guide and runnable SQL sample. Use those separate analyses first, then combine their account-level results when you have a common customer map.
New to the product? See how Combined connects your business data to your AI agent. Already evaluating your stack? Explore the connector guides for setup and dataset details.
Nine worked examples, ready to explore
The planner matches nine cross-app examples covering HubSpot, Salesforce, Stripe, QuickBooks, Chargebee, Intercom, Zendesk, Freshdesk and PostHog. Choose pipeline, renewals, customer support, unpaid balances or retention to narrow the result. Each matched example includes its reporting unit, identity map and calculation checks.
Try Salesforce with Stripe for pipeline and invoices, HubSpot with Chargebee for renewal coverage, or PostHog with Stripe for paid retention. For another combination, the planner gives your agent a schema-discovery assignment and the decisions needed to build the query.
The broader picker uses the published connector library. Catalog references help you plan; the current connection setup, successful sync and source grants determine what your agent can query in your workspace.
Make the answer survive a closer look
Matching customer names is a fragile shortcut. Give your agent a maintained relationship between CRM companies, billing customers and support organizations. Decide what one output row represents, then aggregate independent record sets before joining them. Otherwise, several deals and several invoices can multiply each other's totals.
Try the interactive join lab to see the multiplication and download the corrected SQL. The planner also carries source-specific checks: subscription-level renewal mapping, invoice payment dates, remaining balances, distinct support conversations, and paid recurring service coverage for a fixed retention cohort.
Choose a date range, timezone and acceptable source freshness before running the calculation. Keep money in separate currencies until you have a conversion rule, and distinguish missing data from a verified empty result. These definitions make an answer useful for an actual business decision.
Give your agent the assignment
Copy the generated prompt into your MCP-compatible agent after connecting your sources in Combined. It walks through source discovery, schema inspection, freshness, customer mappings and a bounded read-only calculation. Follow the source setup guide and MCP connection reference to get ready.
Download your selection as Markdown for an agent or JSON for your own tooling. A shared link stores just the chosen app names and analysis. The planner itself needs no credentials or customer-data upload. Runnable examples use synthetic records so you can inspect their behavior before adapting them.
Prefer a stable reference to start from? Download the default HubSpot, Stripe and Intercom plan as Markdown or JSON. For repeatable workflows, add the free Combined agent skills to your client.
Sources and further reading
Explore the documentation behind this guide. Product details checked on September 10, 2026.