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.

Vertex AI Search MCP Server for CrewAI 7 tools — connect in under 2 minutes

Name: Vertex AI Search
Availability: InStock
Author: Vinkius

docs.crewai.com/mcp/overview

Built by Vinkius GDPR 7 Tools Framework

Connect your CrewAI agents to Vertex AI Search through the Vinkius — pass the Edge URL in the `mcps` parameter and every Vertex AI Search tool is auto-discovered at runtime. No credentials to manage, no infrastructure to maintain.

Get MCP Server for AI Agents

ASK AI ABOUT THIS MCP SERVER

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Vinkius supports streamable HTTP and SSE.

python

from crewai import Agent, Task, Crew

agent = Agent(
    role="Vertex AI Search Specialist",
    goal="Help users interact with Vertex AI Search effectively",
    backstory=(
        "You are an expert at leveraging Vertex AI Search tools "
        "for automation and data analysis."
    ),
    # Your Vinkius token — get it at cloud.vinkius.com
    mcps=["https://edge.vinkius.com/[YOUR_TOKEN_HERE]/mcp"],
)

task = Task(
    description=(
        "Explore all available tools in Vertex AI Search "
        "and summarize their capabilities."
    ),
    agent=agent,
    expected_output=(
        "A detailed summary of 7 available tools "
        "and what they can do."
    ),
)

crew = Crew(agents=[agent], tasks=[task])
result = crew.kickoff()
print(result)

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About Vertex AI Search MCP Server

Connect your Vertex AI Search account to any AI agent and harness the power of Google's semantic search technology on your own enterprise data through natural conversation.

When paired with CrewAI, Vertex AI Search becomes a first-class tool in your multi-agent workflows. Each agent in the crew can call Vertex AI Search tools autonomously — one agent queries data, another analyzes results, a third compiles reports — all orchestrated through the Vinkius with zero configuration overhead.

What you can do

Semantic Search — Perform high-quality semantic searches across documents with AI-powered relevance and accuracy
Grounded Answers — Get direct, natural language answers grounded in your private document collection for reliable Q&A
Data Stores — List and browse your enterprise data stores and search engines to manage your searchable datasets
Document Discovery — Browse and list indexed documents within your data store branches directly from your agent
Personalized Recommendations — Retrieve intelligent recommendations based on user interaction events and patterns
Search Engines — View and manage high-level search applications configured for specific business use cases

The Vertex AI Search MCP Server exposes 7 tools through the Vinkius. Connect it to CrewAI in under two minutes — no API keys to rotate, no infrastructure to provision, no vendor lock-in. Your configuration, your data, your control.

How to Connect Vertex AI Search to CrewAI via MCP

Follow these steps to integrate the Vertex AI Search MCP Server with CrewAI.

Install CrewAI

Run pip install crewai

Replace the token

Replace [YOUR_TOKEN_HERE] with your Vinkius token from cloud.vinkius.com

Customize the agent

Adjust the role, goal, and backstory to fit your use case

Run the crew

Run python crew.py — CrewAI auto-discovers 7 tools from Vertex AI Search

Why Use CrewAI with the Vertex AI Search MCP Server

CrewAI Multi-Agent Orchestration Framework provides unique advantages when paired with Vertex AI Search through the Model Context Protocol.

Multi-agent collaboration lets you decompose complex workflows into specialized roles — one agent researches, another analyzes, a third generates reports — each with access to MCP tools

CrewAI's native MCP integration requires zero adapter code: pass the Vinkius Edge URL directly in the `mcps` parameter and agents auto-discover every available tool at runtime

Built-in task delegation and shared memory mean agents can pass context between steps without manual state management, enabling multi-hop reasoning across tool calls

Sequential and hierarchical crew patterns map naturally to real-world workflows: enumerate subdomains → analyze DNS history → check WHOIS records → compile findings into actionable reports

Vertex AI Search + CrewAI Use Cases

Practical scenarios where CrewAI combined with the Vertex AI Search MCP Server delivers measurable value.

Automated multi-step research: a reconnaissance agent queries Vertex AI Search for raw data, then a second analyst agent cross-references findings and flags anomalies — all without human handoff

Scheduled intelligence reports: set up a crew that periodically queries Vertex AI Search, analyzes trends over time, and generates executive briefings in markdown or PDF format

Multi-source enrichment pipelines: chain Vertex AI Search tools with other MCP servers in the same crew, letting agents correlate data across multiple providers in a single workflow

Compliance and audit automation: a compliance agent queries Vertex AI Search against predefined policy rules, generates deviation reports, and routes findings to the appropriate team

Vertex AI Search MCP Tools for CrewAI (7)

These 7 tools become available when you connect Vertex AI Search to CrewAI via MCP:

get_datastore_details

Retrieves configuration and metadata for a specific data store

get_grounded_answer

Returns a natural language response based on your private data. Retrieves an AI-generated answer grounded in the documents of a data store

get_recommendations

Provide a data store ID and user event data as a JSON object. Retrieves personalized recommendations based on user events

list_data_stores

Lists all data stores in the Vertex AI Search collection

list_datastore_documents

Provide data store and branch IDs. Lists all indexed documents within a specific data store branch

list_search_engines

Lists all search engines configured in the collection

search_documents

Provide a data store ID and the query text. Performs a search query across documents in a specific data store

Example Prompts for Vertex AI Search in CrewAI

Ready-to-use prompts you can give your CrewAI agent to start working with Vertex AI Search immediately.

"List all my available data stores in Vertex AI Search."

"Based on our documentation, what is our remote work policy?"

"Search the product catalog for 'blue wireless headphones'."

Troubleshooting Vertex AI Search MCP Server with CrewAI

Common issues when connecting Vertex AI Search to CrewAI through the Vinkius, and how to resolve them.

MCP tools not discovered

Ensure the Edge URL is correct. CrewAI connects lazily when the crew starts — check console output.

Agent not using tools

Make the task description specific. Instead of "do something", say "Use the available tools to list contacts".

Timeout errors

CrewAI has a 10s connection timeout by default. Ensure your network can reach the Edge URL.

Rate limiting or 429 errors

The Vinkius enforces per-token rate limits. Check your subscription tier and request quota in the dashboard. Upgrade if you need higher throughput.

Vertex AI Search + CrewAI FAQ

Common questions about integrating Vertex AI Search MCP Server with CrewAI.

How does CrewAI discover and connect to MCP tools?

CrewAI connects to MCP servers lazily — when the crew starts, each agent resolves its MCP URLs and fetches the tool catalog via the standard tools/list method. This means tools are always fresh and reflect the server's current capabilities. No tool schemas need to be hardcoded.

Can different agents in the same crew use different MCP servers?

Yes. Each agent has its own mcps list, so you can assign specific servers to specific roles. For example, a reconnaissance agent might use a domain intelligence server while an analysis agent uses a vulnerability database server.

What happens when an MCP tool call fails during a crew run?

CrewAI wraps tool failures as context for the agent. The LLM receives the error message and can decide to retry with different parameters, fall back to a different tool, or mark the task as partially complete. This resilience is critical for production workflows.

Can CrewAI agents call multiple MCP tools in parallel?

CrewAI agents execute tool calls sequentially within a single reasoning step. However, you can run multiple agents in parallel using process=Process.parallel, each calling different MCP tools concurrently. This is ideal for workflows where separate data sources need to be queried simultaneously.

Can I run CrewAI crews on a schedule (cron)?

Yes. CrewAI crews are standard Python scripts, so you can invoke them via cron, Airflow, Celery, or any task scheduler. The crew.kickoff() method runs synchronously by default, making it straightforward to integrate into existing pipelines.