A MCP server for Vertex AI Search
For Vertex AI Search MCP server is a locally run integration for AI assistants that speak the Model Context Protocol. A MCP server for Vertex AI Search.
This solution uses Gemini with Vertex AI grounding to search documents using your private data. Grounding improves the quality of search results by grounding Gemini's responses in your data stored in Vertex AI Datastore. We can integrate one or multiple Vertex AI data stores to the MCP server. For more details on grounding, refer to Vertex AI Grounding Documentation.
Installation goes through your MCP client rather than a global install: point it at git on PyPI and it is fetched when the client starts. The copy-paste blocks for Claude Desktop, Claude Code and Cursor are further down this page.
Once For Vertex AI Search is connected, these are the calls the assistant has available:
server — server.name: The name of the MCP servermodel — model.model_name: The name of the Vertex AI modelmodel.project_id — The project ID of the Vertex AI modelmodel.location — The location of the model (e.g. us-central1)model.impersonate_service_account — The service account to impersonatemodel.generate_content_config — The configuration for the generate content APIdata_stores — The list of Vertex AI data storesdata_stores.project_id — The project ID of the Vertex AI data storedata_stores.location — The location of the Vertex AI data store (e.g. us)data_stores.datastore_id — The ID of the Vertex AI data storedata_stores.tool_name — The name of the tooldata_stores.description — The description of the Vertex AI data storeThis sits in the developer tooling group, where several servers overlap in what they claim to do but differ sharply once you actually set them up. For Vertex AI Search's toolset — server, model, model.project_id and 10 more — is a fair guide to whether it matches your workflow. It is maintained by ubie-oss; worth a glance at recent repository activity before you build anything load-bearing on it.
This entry was verified against For Vertex AI Search's own documentation before publication; SyncDev keeps the directory reviewed rather than auto-generated.
| Tool | What it does |
|---|---|
| server | server.name: The name of the MCP server |
| model | model.model_name: The name of the Vertex AI model |
| model.project_id | The project ID of the Vertex AI model |
| model.location | The location of the model (e.g. us-central1) |
| model.impersonate_service_account | The service account to impersonate |
| model.generate_content_config | The configuration for the generate content API |
| data_stores | The list of Vertex AI data stores |
| data_stores.project_id | The project ID of the Vertex AI data store |
| data_stores.location | The location of the Vertex AI data store (e.g. us) |
| data_stores.datastore_id | The ID of the Vertex AI data store |
| data_stores.tool_name | The name of the tool |
| data_stores.description | The description of the Vertex AI data store |
| Prerequisites | The Prerequisites tool exposed by this server. |
{
"mcpServers": {
"vertexai-search": {
"command": "uvx",
"args": ["git"]
}
}
}Add to claude_desktop_config.json, then restart Claude Desktop.
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