How to Use the Vertex AI Search MCP in CrewAI

Q: Can CrewAI manage different knowledge sources for Vertex AI Search?

Yes. You can use listdatastores to enumerate all available data stores, allowing your crew to select the correct source based on the task at hand.

Q: What happens when an agent needs recommendations using Vertex AI Search and CrewAI?

The agent calls getrecommendations, passing a data store ID and user event JSON. The server returns personalized suggestions that guide the autonomous workflow.

Q: Does the Vertex AI Search MCP Server help audit document status in CrewAI?

Yes. Agents can use listdatastoredocuments to check exactly which documents are indexed within a specific branch, ensuring your team has the most current source material.

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Connect Vertex AI Search MCP to CrewAI

Create your Vinkius account to connect Vertex AI Search to CrewAI and route execution through our secure gateway. The platform manages server hosting, runtime updates, and security layers. Configuration requires no manual server provisioning.

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Setup Vertex AI Search with CrewAI

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Claude

Perplexity

Knowledge Synthesis via Grounding

Have Agent A research a topic, then pass the findings to Agent B. Agent B uses `get_grounded_answer` to synthesize a final answer based on private data, ensuring all claims are source-backed. This structured handoff makes sure your autonomous operations don't hallucinate; they always reference specific documents.

Contextual Decision Making

When an agent needs to make a decision based on user history, it calls `get_recommendations`. You provide the data store ID and a JSON payload of user actions. The system returns actionable recommendations. This is perfect for complex monitoring agents that need to suggest next steps.

Automated Information Retrieval

An agent can run a full research cycle: first, calling `list_data_stores` to identify sources. Then, it uses `search_documents` against the targeted store ID and query text. The crew structure handles this sequential process automatically without needing human intervention.

Setup guide

Set up Vertex AI Search MCP in CrewAI

Prerequisites

Python 3.10+ installed
crewai package (pip install crewai)
Active Vinkius subscription with a valid endpoint token

1

Install CrewAI
Run pip install crewai to install the framework. MCP support is built-in via the mcps parameter.
2

Add the MCP URL to your agent
Pass your Vinkius endpoint directly to the mcps list. Replace [YOUR_TOKEN_HERE] with your token from cloud.vinkius.com. CrewAI handles tool discovery and caching automatically.
3

Kick off your crew
Create a Crew with your agent and tasks. Call crew.kickoff() — the agent will automatically invoke Vertex AI Search tools as needed.

crew.py

from crewai import Agent, Task, Crew

agent = Agent(
    role="Vertex AI Search Analyst",
    goal="Access and analyze Vertex AI Search data via MCP.",
    backstory="Expert analyst with direct Vertex AI Search access.",
    mcps=[
        "https://edge.vinkius.com/[YOUR_TOKEN_HERE]/mcp"
    ],
)

task = Task(
    description="List recent Vertex AI Search transactions",
    agent=agent,
    expected_output="A summary of recent activity",
)

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

Get your connection token →

Prerequisites

Python 3.10+ installed
crewai + crewai-tools packages
Active Vinkius subscription with a valid endpoint token

1

Install dependencies
Run pip install crewai crewai-tools. The MCPServerAdapter handles lifecycle management and tool conversion.
2

Connect with MCPServerAdapter
Use MCPServerAdapter as a context manager with SseServerParameters pointing to your Vinkius endpoint. The adapter automatically manages connection lifecycle.
3

Assign tools and run
Pass the returned mcp_tools to your agent's tools parameter. The adapter converts MCP tools to native BaseTool objects compatible with all CrewAI agents.

crew.py

from crewai import Agent, Task, Crew
from crewai_tools import MCPServerAdapter
from mcp import SseServerParameters

server_params = SseServerParameters(
    url="https://edge.vinkius.com/[YOUR_TOKEN_HERE]/mcp"
)

with MCPServerAdapter(server_params) as mcp_tools:
    agent = Agent(
        role="Vertex AI Search Analyst",
        goal="Access and analyze Vertex AI Search data via MCP.",
        backstory="Expert analyst with direct Vertex AI Search access.",
        tools=mcp_tools,
    )

    task = Task(
        description="List recent Vertex AI Search transactions",
        agent=agent,
        expected_output="A summary of recent activity",
    )

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

Get your connection token →

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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Common questions about Vertex AI Search MCP in CrewAI

Agents collaborate by passing data between them. For instance, one agent runs `search_documents`, and the next agent uses that output as context to generate a summary using `get_grounded_answer`.

Yes. You can use `list_data_stores` to enumerate all available data stores, allowing your crew to select the correct source based on the task at hand.

The agent calls `get_recommendations`, passing a data store ID and user event JSON. The server returns personalized suggestions that guide the autonomous workflow.

The MCP Server touches private textual documents and structured user event metadata. Since agents are acting autonomously, defining clear access roles for these data types is crucial.

Yes. Agents can use `list_datastore_documents` to check exactly which documents are indexed within a specific branch, ensuring your team has the most current source material.

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