Vertex AI Search MCP. Ground answers using your internal documentation.
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Vertex AI Search gives your agent access to semantic search across your private, enterprise data. It uses Google's grounding technology to pull direct answers from internal documents instead of relying on general knowledge.
You can list data stores, query specific documents, or get personalized recommendations—all via a conversational API call.
What your AI agents can do
Get datastore details
Retrieves configuration and metadata for a specific data store ID so you know what the source is.
Get grounded answer
Returns a natural language answer, explicitly citing documents from your private data stores to prevent hallucination.
Get recommendations
Uses user event data and a data store ID to generate intelligent suggestions for the next steps or related content.
The agent calls get_grounded_answer to pull a factual answer directly from your company's document collection.
You use list_data_stores first, then run search_documents, to find content scattered across multiple departmental sources.
The tool list_datastore_documents lets your agent list every document within a specific data store branch ID for auditing or review.
By invoking get_recommendations, the agent reads user event patterns to suggest relevant internal documents or next steps.
The tool list_search_engines lets your agent view and manage all high-level search apps configured for different business units.
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Vertex AI Search: 7 Tools for Data Retrieval
These tools allow your AI client to systematically discover, query, and retrieve specific information from highly structured internal data stores.
019d761cget datastore details
Retrieves configuration and metadata for a specific data store ID so you know what the source is.
019d761cget grounded answer
Returns a natural language answer, explicitly citing documents from your private data stores to prevent hallucination.
019d761cget recommendations
Uses user event data and a data store ID to generate intelligent suggestions for the next steps or related content.
019d761clist data stores
Lists every available data store configured in the Vertex AI Search collection so you can see all sources.
019d761clist datastore documents
Provides a list of indexed documents within a specific data store branch ID for auditing or review.
019d761clist search engines
Lists all high-level search applications configured in the collection, letting you see what searches are available.
019d761csearch documents
Performs a direct query across documents in a specific data store ID and returns relevant document excerpts.
Choose How to Get Started
Build a custom MCP for your own tools, or connect a ready-made integration from our catalog.
Build Your Own
Turn any API into an MCP. Import a spec, define Agent Skills, or deploy with MCPFusion.
- Import from OpenAPI, Swagger, or YAML specs
- Create Agent Skills with progressive disclosure
- Deploy to edge with MCPFusion framework
- Built in DLP, auth, and compliance on every call
- Real time usage dashboard and cost metering
- Publish to catalog or keep private
Make Your AI Do More
Start with Vertex AI Search, then connect any of our 4,700+ other servers whenever your AI needs more. One click, no limits.
- Use this MCP plus 4,700+ others, all in one place
- Add new capabilities to your AI anytime you want
- Every connection is secured and compliant automatically
- Track usage and costs across all your servers
- Works with Claude, ChatGPT, Cursor, and more
- New servers added to the catalog every week
What you can do with this MCP connector
Vertex AI Search hooks your agent right into your company's private data stack. Forget general web searches; this server uses Google’s grounding tech to pull answers only from your internal documents, stopping bad hallucinations cold.
To map out what sources you have: You start by using list_data_stores, which spits out a list of every available knowledge source configured in the entire collection. If you need to dig into one specific store's details—like figuring out its metadata or configuration parameters—you run get_datastore_details with a particular data store ID.
To find content within those stores: You can use search_documents for a direct query against documents in a specified data store. This tool doesn't just give you a hit list; it returns relevant document excerpts, letting your agent pinpoint the exact text that matches the user's request. For comprehensive auditing or review purposes, if you need to see every single indexed file within a specific branch of a data store, you call list_datastore_documents with the correct data store branch ID.
To get definitive answers: When your agent needs an answer and doesn't just want excerpts, it calls get_grounded_answer. This is crucial: it returns a natural language response that explicitly cites documents from your private stores. It prevents general knowledge gaps by forcing the answer to be fact-checked against your content.
To view system configuration: If you need an overview of all high-level search applications set up across different business units, you use list_search_engines. This lets your agent see and manage every configured application within the collection.
For context-aware suggestions: You don't always know what the user needs next. By invoking get_recommendations, the agent reads patterns from user event data alongside a specific data store ID. It then suggests relevant internal documents or logical next steps, guiding the conversation without needing explicit instructions.
Your agent uses these tools together to transform your massive pile of documentation into an expert teammate that speaks natural language and points you straight to the source material.
How Vertex AI Search MCP Works
- 1 Subscribe to the server and provide your Google Cloud Project ID, Location, and Access Token.
- 2 First, run
list_data_storesorlist_search_enginesto map out what data sources are available in the collection. - 3 Finally, you execute a specific query using
get_grounded_answer(for answers) orsearch_documents(for excerpts), providing the necessary data store ID.
The bottom line is, your agent doesn't guess; it uses structured calls to find and cite information from your internal sources.
Who Is Vertex AI Search MCP For?
Data Scientists and Knowledge Managers need this. They waste too much time manually checking multiple documentation systems (Confluence, SharePoint, etc.) just to answer one question. Developers building internal AI tools also use it constantly—they need a reliable way to ground their agents in real company data without custom indexing.
Uses list_data_stores and get_grounded_answer to surface relevant policies or procedures from massive, scattered document repositories through simple chat.
Integrates this MCP Server into an application pipeline. They use all seven tools to build grounded AI features that reference internal knowledge bases without manual indexing.
Tests and refines search relevance by running search_documents on specific data stores, verifying the quality of the generated answers via get_grounded_answer.
What Changes When You Connect
- Get factual answers instantly: Instead of reading through 50 pages of PDFs, call
get_grounded_answerand get a concise answer cited directly from your private documents. No more guessing games with generic AI knowledge. - Full data visibility: Use
list_data_storesto map out every single source (HR policies, product catalogs, legal docs) available in the system before you write a single query. - Deep document inspection: If you need proof of what's indexed, run
list_datastore_documentswith specific IDs. It lets you audit exactly which documents are searchable without running a full search. - Proactive guidance: Run
get_recommendationsby feeding in user activity data. Your agent won't just answer; it will suggest the next most useful document or process based on history. - Structured search results: When you need snippets, not paragraphs, run
search_documents. This tool pulls direct quotes from your source materials for verification.
Real-World Use Cases
A new employee needs to know the vacation policy.
The agent uses list_data_stores and discovers 'HR Policies'. It then calls get_grounded_answer with the query, receiving a precise answer about PTO accrual, citing the exact internal HR document section.
A developer needs to test search relevance for a product line.
The dev uses list_data_stores (finding 'Product Catalog'), then runs search_documents, and finally checks get_datastore_details. This confirms the catalog is indexed correctly before building an app.
A support agent needs to check which documentation exists for a niche feature.
The agent calls list_search_engines to see if there's a dedicated 'Feature X' search app. If not, it uses get_datastore_details on the general tech store ID to scope out available information.
A user asks for help after browsing several product pages.
The agent detects the user's pattern (viewed 3 headphones, checked warranty page). It runs get_recommendations and suggests 'You might also need to check our accessories guide.'
The Tradeoffs
Assuming everything is searchable
A user just asks the agent, 'What's the policy?' without telling it where to look. The agent might fail or give a vague answer.
→
Always start by running list_data_stores first. This lets you select the correct source (e.g., 'HR Policies') before using tools like get_grounded_answer. Context is everything.
Relying on general web knowledge
The agent gives a generic answer about remote work, even though the company has specific policies.
→
You must use get_grounded_answer against your internal data store. This forces the response to be sourced only from what you've provided, guaranteeing accuracy.
Over-querying documents blindly
Calling list_datastore_documents without knowing which branch ID you need, resulting in a massive, unusable list of file names.
→
Always scope your document listing. Use the output from get_datastore_details to identify the correct data store and then use that ID when calling list_datastore_documents.
When It Fits, When It Doesn't
Use this MCP Server if your primary pain point is information retrieval across a diverse, disconnected body of private corporate documents. If your goal is simply chatting with an LLM or summarizing text you've already copied, don't use it—a general-purpose chat model works fine. However, if the answer must be verifiable and sourced from internal policies (HR manuals, engineering specs, etc.), this is non-negotiable. If you only need to search one small, contained database, a simpler document index might cut it. But for cross-departmental knowledge retrieval that requires discovery, selection, and grounding—this suite of tools is the right call.
Independent Platform Disclaimer: Vinkius is an independent platform and is not affiliated with, endorsed by, sponsored by, verified by, or otherwise authorized by Vertex AI Search. All third-party trademarks, logos, and brand names are the property of their respective owners. Their use on this website is strictly for informational purposes to identify service compatibility and interoperability.
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Works with Claude, ChatGPT, Cursor, and more
The Model Context Protocol standardizes how applications expose capabilities to LLMs. Instead of operating in isolation, your AI gains direct access to external platforms, live data, and real-world actions through secure, standardized connections.
This server provides 7 capabilities that interface natively with Claude, ChatGPT, Cursor, and any MCP client. No middleware. No custom integration required.
Available Capabilities
Finding answers in internal documents shouldn't feel like detective work.
Today, finding a single answer means jumping between SharePoint, Confluence, Jira tickets, and PDF manuals. You copy names, you search keywords, you cross-reference dates. It takes 20 minutes of clicking through different tabs just to confirm one policy point.
With the Vertex AI Search MCP Server, your agent handles that complexity for you. Instead of manual searching, you simply ask a question, and the agent uses `get_grounded_answer` to pull the exact answer and cite the source document in seconds.
Vertex AI Search MCP Server: Structured Retrieval.
The manual steps that vanish are credential gathering, figuring out which documentation repository holds the answer, and then manually cross-referencing multiple search results. You don't have to manage those IDs or the complex query syntax.
Your agent does it all—it finds the right data store (via `list_data_stores`), queries it (`search_documents`), and gives you a clean, grounded answer. It just works.
Common Questions About Vertex AI Search MCP
How do I find out what sources are available with list_data_stores? +
Run list_data_stores first. This call returns a complete inventory of all data stores—like HR policies or product catalogs—that the agent can query against.
What is the difference between search_documents and get_grounded_answer? +
search_documents gives you snippets (raw text) from your documents. get_grounded_answer takes those snippets, reads them, and writes a full, natural language answer for you.
Do I need to know the data store ID before running any query? +
Yes, almost always. The tools require specific identifiers (like the Data Store ID) to scope the search correctly. Use get_datastore_details if you're unsure about a source.
Can I check what documents are indexed in my system using list_datastore_documents? +
Yes, that's exactly what it does. You provide the data store and branch IDs to list_datastore_documents, and it returns a full list of all indexable files.
What authentication details must I provide when using list_data_stores? +
You need to provide your Google Cloud Project ID, Location, and an Access Token. These credentials allow your AI agent to connect securely to the Vertex AI Search collection and read all available data store metadata.
How do I use list_search_engines to see configured search applications? +
The tool lists every high-level search application set up within your account. This shows you preconfigured endpoints for specific business needs, separate from the raw data stores themselves.
What information can I get about a store using get_datastore_details? +
It retrieves detailed configuration and metadata for one specific data store. This is helpful when you need to verify parameters or check the status of a dataset before running complex queries.
What input structure does get_recommendations require? +
The tool requires both a Data Store ID and user event data formatted as a JSON object. Providing detailed user interactions allows your agent to generate personalized, relevant suggestions for the user.
Can I get direct answers from my documents without reading through them? +
Yes. Using the get_grounded_answer tool, your AI agent can process a natural language question and return a precise answer based specifically on the content within your Vertex AI Search data stores. This grounding ensures high accuracy and reduces hallucinations by sticking to your private data as the source of truth.
How do I know which data stores are available to search? +
Ask your agent to list your data stores. It will return all configured data stores in your collection along with their IDs and names. You can then use these IDs to perform targeted semantic searches or browse specific document branches.
Can I use this for product recommendations on my website? +
Absolutely. The get_recommendations tool allows your agent to retrieve personalized recommendations by providing user event data. This is ideal for testing recommendation engines and surfacing relevant content or products to users based on their historical behavior.
Use it with your favorite AI tools
Connect this server to Cursor, Claude, VS Code, and more.
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