> For the complete documentation index, see [llms.txt](https://docs.rootcause.ai/llms.txt). Markdown versions of documentation pages are available by appending `.md` to page URLs; this page is available as [Markdown](https://docs.rootcause.ai/api-and-integrations/api-access/mcp-integration/mcp-capabilities.md).

# What You Can Do

A connected assistant works with the same objects the platform UI shows: sources, datasets, the [ontology](/user-guide/ontology-concepts.md), [Digital Twins](/user-guide/creating-digital-twin.md), simulations, reports, and [applications](/more-details/applications.md). Ask in plain language; tool names below are what the assistant reports while working, not something you type.

## Terminology

The same two words mean the same thing everywhere — the UI, the REST API, and these tools. A **Source** is raw ingested data (an uploaded file, a connector import, an extracted document), identified by `sourceId`. A **Dataset** is a derived, queryable table over one or more sources (filters, joins, aggregations, SQL), identified by `datasetId`.

Sources belong to the **organisation** and are linked into workspaces; the same source can appear in several. Everything else lives in a single **workspace**. If a source is not yet linked into the workspace, the assistant links it first (`link_source_to_workspace`).

## Explore what a workspace contains

* "What workspaces do we have?" (`list_workspaces`)
* "What is in this workspace?" for a one-call inventory of sources, datasets, ontology, and twins (`get_workspace_overview`)
* "What data exists in the org that is not linked here?" (`list_org_sources`)
* "Show me the schema of the shipments table" (`get_source_schema`)

## Ask questions of your data

* "Revenue by region last quarter" becomes an [Anchor SQL](/api-and-integrations/anchor-sql.md) query over ontology concepts — the ontology plans the joins and time alignment (`query_data`)
* Raw SQL against the underlying sources, for the queries Anchor SQL cannot express (`query_sql`)
* "Show me the first rows" (`get_source_preview`)
* "Chart churn by tenure" (`create_chart`)

For metric questions, the assistant reuses the workspace's defined metrics by name rather than re-deriving them, and states the definition it used.

## Bring new data in

Each route produces a new source. Some of them run in the background and hand back a run id to poll with `check_background_runs` — connector imports (`import_from_connector`) and all three non-preview modes of `documents_to_source` — while `import_source` and `research_to_table` wait for the work and return the finished source directly:

* Tables from connected databases and storage (`import_from_connector`)
* External APIs (`register_connector`, `import_source`)
* Structured data out of documents in the workspace (`documents_to_source`)
* Web pages or a research prompt into a table, on deployments with web research enabled (`research_to_table`)

## Shape data for analysis

The server distinguishes three ways of combining sources: joins (widening on a shared key), concatenation (stacking same-shaped files), and time alignment (bucketing or as-of matching). The assistant:

* Probes real values before trusting a join key (`probe_join_keys`)
* Builds and modifies datasets (`build_dataset`, `modify_dataset`)
* Maintains the ontology that makes columns joinable across sources (`query_ontology`, `modify_ontology`)

## Do causal work

* Run causal discovery on a dataset (`run_causal_discovery`)
* Create and train Digital Twins (`create_digital_twin`, `train_digital_twin`)
* Inject domain knowledge: forced edges, temporal order (`update_digital_twin_relationships`)
* Review a trained twin's quality before trusting it (`review_digital_twin`)

Discovery and training are background jobs: the assistant starts them and reports back when they finish.

## Simulate and predict

* "What if we raise price 10 percent?" (`query_digital_twin`)
* "What drives churn?" (`query_causal_graph`)
* Interventions, optimisations, forecasts, root cause analyses
* "Does marketing drive demand in every region?" across a multi-environment twin (`compare_environment_graphs`)

## Produce reports and applications

* Analytical reports from workspace evidence (`create_report`), revised in place (`edit_report`)
* Interactive Applications: runnable flows with dashboards, forms, and viewer-adjustable simulations (`create_application`, `publish_application`). See [Applications](/more-details/applications.md).

## Monitor usage

Ask usage questions directly: AI spend over time, top spenders by user or workspace, failure rates, security-relevant events (`query_usage_monitor`). Reads the same organisation audit log as the Usage Monitor page.

## Pacing

* **Search before import.** The assistant checks existing sources, datasets, and registered connectors before fetching anything external.
* **Background jobs return run ids, not results.** The assistant polls for completion before claiming the data or model exists.

For which actions need your confirmation, see [Permissions and Safety](/api-and-integrations/api-access/mcp-integration/mcp-permissions-and-safety.md). For the packaged procedures behind the involved flows, see [Guided Skills](/api-and-integrations/api-access/mcp-integration/mcp-skills.md).