> ## Documentation Index
> Fetch the complete documentation index at: https://docs.agentruntime.io/llms.txt
> Use this file to discover all available pages before exploring further.

# Google Search connector

> Query your Vertex AI Search app from AgentRuntime workflows for grounded retrieval.

The **google-search** MCP adapter calls [Vertex AI Search](https://cloud.google.com/generative-ai-app-builder/docs/enterprise-search-introduction) (Discovery Engine) via `searchLite`. Use it in workflow **mcp\_call** steps to retrieve indexed content from your own data stores and feed ranked results into LLM steps for RAG-style answers. Unlike web search, results come only from content you have indexed in your Vertex AI Search app.

## Prerequisites

* A Google Cloud project with Vertex AI Search (Discovery Engine) configured and an indexed search app
* A Google Cloud API key authorized for Discovery Engine
* A **Google Workspace connection** in AgentRuntime (OAuth via the **Google account** card on **Connections**) or a connection profile with search credentials
* **project\_contributor** access in your workspace

## Connect in AgentRuntime

<Steps>
  <Step title="Connect Google">
    On **Connections**, use the **Google account** card and complete OAuth if your deployment binds search through Workspace. See [Google Workspace setup](/integrations/google-workspace) and [Connections](/integrations/connections).
  </Step>

  <Step title="Enable Search">
    On your Google connection, enable the **Search** service, or create a connection profile with your Vertex AI Search API key, project ID, and app ID.
  </Step>

  <Step title="Add google-search MCP instance">
    Go to **MCP**, click **Add instance** for **google-search**. Open **Instance config**, wire your connection, and set `ProjectID`, `AppID`, and `APIKey` in the instance config if not inherited from the connection.
  </Step>

  <Step title="Test in a workflow">
    Smoke-test with **mcp\_call** in Workflow Studio. Test with a simple `search` query before production workflows.
  </Step>
</Steps>

## What you can build

* **Support knowledge lookup** — Customer question in webhook → `search` against your help-center index → LLM answer with citations from `results`.
* **Internal doc assistant** — User prompt → `search` with `user_pseudo_id` for personalization → rank and summarize top hits for a chat response.
* **Compliance check** — Policy change draft → `search` for related procedures → LLM gap analysis → **human\_task** for legal review.
* **Product catalog Q\&A** — **google-sheets** SKU sync to search index → `search` by feature or part number → return matching product snippets to the caller.

## Tools

| Tool     | Description                                                             |
| -------- | ----------------------------------------------------------------------- |
| `search` | Run a query against Vertex AI Search using Discovery Engine searchLite. |

## Example

Retrieve relevant documentation before an LLM summarizes an answer:

```json theme={null}
{
  "id": "search-kb",
  "type": "mcp_call",
  "name": "Search knowledge base",
  "tool_name": "search",
  "tool_args": {
    "query": "{{input.user_question}}",
    "user_pseudo_id": "{{input.session_id}}"
  },
  "depends_on": [],
  "timeout_s": 30
}
```

## Configuration

Beyond OAuth, the adapter requires Vertex AI Search identifiers:

| Key         | Required | Description                                                         |
| ----------- | -------- | ------------------------------------------------------------------- |
| `APIKey`    | Yes      | Google Cloud API key authorized for Vertex AI Search                |
| `ProjectID` | Yes      | Google Cloud project ID that owns the search app                    |
| `AppID`     | Yes      | Vertex AI Search engine (app) ID used as the collection engine name |
| `APIBase`   | No       | Discovery Engine API base URL override                              |

## Troubleshooting

| Issue                        | Fix                                                                                          |
| ---------------------------- | -------------------------------------------------------------------------------------------- |
| `403` API key not authorized | Enable Discovery Engine API and restrict the key to the correct project                      |
| Empty `results`              | Confirm the search app index is built and the query matches indexed content                  |
| Wrong tenant or project      | Verify `ProjectID` and `AppID` match the engine in Google Cloud Console                      |
| Pagination needed            | Use `next_page_token` from the response in a follow-up call if your workflow supports paging |

## Related

* [Integrations quickstart](/integrations/quickstart)
* [Google Workspace setup](/integrations/google-workspace)
* [Connector catalog](/integrations/connector-catalog)
* [Troubleshooting](/platform/troubleshooting)