How to Use the Zenserp MCP in CrewAI

Q: Can CrewAI compare different search engine results using Zenserp?

Yes. You can structure a crew where Agent A uses searchbing, and Agent B immediately follows up with searchduckduckgo. The comparison happens because the data is passed sequentially through the workflow.

Q: Can my crew gather local map data from Zenserp?

You can absolutely make an 'Investigation Agent' use searchmaps. This allows your multi-agent team to pull structured business listings and reviews for a specific location.

Q: Are there limitations on the data types retrieved by Zenserp with CrewAI?

The tools allow everything from text (via searchnews) to media links (searchyoutube). The limitation is only what you define in your agent's specialized role.

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Create your Vinkius account to connect Zenserp 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 Zenserp with CrewAI

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Specialized Research Roles via MCP Server

You define a 'Research Agent' whose sole job is to use `search_bing` and `search_duckduckgo`. This agent collects diverse search data, which a separate 'Analysis Agent' then consumes for deeper insights. The shared memory ensures that the results from both engines are available when the final report is generated.

Autonomous Media Monitoring with CrewAI

Set up a dedicated monitoring crew. One agent can use `search_news` to gather titles and snippets, while another uses `search_youtube` to pull video data. The system coordinates these specialized searches automatically. The moderator agent watches both sessions, ensuring the entire operation completes without human intervention.

Global Product Intelligence Gathering

For market analysis, you can task your crew with multiple steps: first using `search_shopping` for pricing, then following up with `search_yandex` for regional comparisons. This systematic approach covers different geographical needs. The MCP Server provides all the tools—from `search_google` to `search_videos`—to allow specialized agents to work autonomously.

Setup guide

Set up Zenserp 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 Zenserp tools as needed.

crew.py

from crewai import Agent, Task, Crew

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

task = Task(
    description="List recent Zenserp 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="Zenserp Analyst",
        goal="Access and analyze Zenserp data via MCP.",
        backstory="Expert analyst with direct Zenserp access.",
        tools=mcp_tools,
    )

    task = Task(
        description="List recent Zenserp 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 Zenserp. 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 Zenserp MCP in CrewAI

You pass the URL containing Zenserp's tools directly to your Agent definitions. This allows specialized agents—like 'The Search Specialist'—to execute `search_google` and feed results into the team’s shared memory.

Yes. You can structure a crew where Agent A uses `search_bing`, and Agent B immediately follows up with `search_duckduckgo`. The comparison happens because the data is passed sequentially through the workflow.

This MCP Server touches search query strings and location data. Because your operations are autonomous, it's crucial to manage how the agent uses that sensitive input.

You can absolutely make an 'Investigation Agent' use `search_maps`. This allows your multi-agent team to pull structured business listings and reviews for a specific location.

The tools allow everything from text (via `search_news`) to media links (`search_youtube`). The limitation is only what you define in your agent's specialized role.

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