How to Use the ValueSERP MCP in CrewAI

Q: How does ValueSERP help CrewAI conduct market analysis?

You assign roles: one agent calls googleshoppingsearch, and another analyzes the retrieved prices. This collaborative approach lets your crew build detailed reports on product availability and pricing trends.

Q: Can my crew use ValueSERP to analyze news trends?

Yes. You can assign an agent specifically to monitor googlenewssearch. This allows the 'Moderator' agent to continuously watch for breaking stories and incorporate them into the final output.

Q: If I need academic validation, how does ValueSERP help CrewAI?

You set up an agent to use googlescholarsearch. This specialized agent gathers abstracts and publications, providing a verifiable source of truth that the other crew members can build their analysis around.

Q: Does ValueSERP support complex search criteria for CrewAI?

It does. You can use customserpsearch to pass highly customized parameters via a JSON object, allowing your agents to execute very specific and targeted searches across the web.

ValueSERP for CrewAI: Specialized agents collaborating on multi-source web analysis and reporting.

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Setup ValueSERP with CrewAI

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Collaborative Market Research

Build a crew where Agent A uses `google_shopping_search` to gather product data, while Agent B simultaneously runs `google_news_search` for competitive context. They both share the findings. This role-based specialization ensures that market reports are comprehensive; one agent handles pricing metrics, and another focuses on real-time events.

Comprehensive Information Gathering

A specialized 'Researcher' agent can execute a full sequence: first, running `google_search` for general concepts. Second, it calls `get_related_questions` to refine the scope. Third, it uses `google_image_search` to gather visual evidence. The monitor agent watches this entire process, ensuring all three data types are collected and passed to the final writer agent.

Targeted Local Intelligence

One dedicated 'Analyst' agent can handle local deep dives. It uses `google_places_search` for coordinates and then cross-references that location with a general search using `google_search`. The results are automatically compiled into a single, actionable intelligence brief without human intervention.

Setup guide

Set up ValueSERP 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 ValueSERP tools as needed.

crew.py

from crewai import Agent, Task, Crew

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

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

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

You assign roles: one agent calls `google_shopping_search`, and another analyzes the retrieved prices. This collaborative approach lets your crew build detailed reports on product availability and pricing trends.

This MCP Server accesses a wide range of public web information, including organic search results, local business listings, academic abstracts, shopping metrics, and video content URLs. It's all read-only.

Yes. You can assign an agent specifically to monitor `google_news_search`. This allows the 'Moderator' agent to continuously watch for breaking stories and incorporate them into the final output.

You set up an agent to use `google_scholar_search`. This specialized agent gathers abstracts and publications, providing a verifiable source of truth that the other crew members can build their analysis around.

It does. You can use `custom_serp_search` to pass highly customized parameters via a JSON object, allowing your agents to execute very specific and targeted searches across the web.

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