How to Use the Clearscope MCP in CrewAI

Q: How do I assign different Clearscope tools to specific agents in CrewAI?

When defining your Agent in CrewAI, you can use the toolfilter argument within the MCPServerHTTP configuration. This lets you expose only certain tools, like getkeywordresearch, to a specific agent, enforcing role specialization.

Q: Can my CrewAI agents collaborate on a single Clearscope report?

Yes. An agent can use createreport and share the report ID through CrewAI's shared context. Other agents can then use that ID with tools like getreportdetails or getoutline to work on the same report.

Deploy an autonomous SEO team on Clearscope with CrewAI. Assign roles for research, analysis, and content creation.

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Create your Vinkius account to connect Clearscope 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 Clearscope with CrewAI

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Assemble an SEO Research Crew

Stop doing manual keyword and competitor research. With CrewAI, you can assign roles to different agents. An 'Analyst' agent can use the `get_keyword_research` tool to identify target terms. It then passes those terms to a 'Researcher' agent via shared memory. The Researcher agent's job is to take those keywords and execute `list_competitors` to find who is already ranking. This division of labor lets each agent focus on a specific task, creating a highly efficient, automated research process.

Run a Content Quality Assurance Team

Use a CrewAI team to enforce content quality standards. A 'Writer' agent (or a human) produces a draft. Then, a 'QA' agent takes over. Its only job is to use the `grade_content` tool to score the text and `list_terms` to check for keyword density. If the content doesn't meet the standards defined in the `get_brief` tool, the QA agent sends it back to the Writer with specific feedback for revision. This creates a closed-loop system for producing high-quality, optimized content without you having to manage the back-and-forth.

Deploy an Autonomous Monitoring Crew

Your CrewAI agents can act as a tireless monitoring system for your SERP rankings. Configure a 'Scout' agent to periodically run `create_report` for your most important keywords. It passes the results to a 'Watchdog' agent, whose job is to check the output of `get_report_details`. If the Watchdog detects a significant change in ranking or score, it can trigger an alert or assign a new task to an Analyst agent to investigate further. This is how you build an autonomous operations team with this Clearscope MCP Server.

Setup guide

Set up Clearscope 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 Clearscope tools as needed.

crew.py

from crewai import Agent, Task, Crew

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

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

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

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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="Clearscope Analyst",
        goal="Access and analyze Clearscope data via MCP.",
        backstory="Expert analyst with direct Clearscope access.",
        tools=mcp_tools,
    )

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

When defining your Agent in CrewAI, you can use the `tool_filter` argument within the `MCPServerHTTP` configuration. This lets you expose only certain tools, like `get_keyword_research`, to a specific agent, enforcing role specialization.

Yes. An agent can use `create_report` and share the report ID through CrewAI's shared context. Other agents can then use that ID with tools like `get_report_details` or `get_outline` to work on the same report.

A common pattern is a sequential crew: a 'Researcher' agent finds keywords with `get_keyword_research`, an 'Outliner' agent builds a structure with `get_outline`, and a 'Reviewer' agent scores the final text with `grade_content`.

Yes, it works with both. You can set up a simple sequential workflow for content creation or a hierarchical one where a manager agent delegates research and grading tasks to subordinate agents.

Your agent's access is scoped by a single endpoint token. Every operation, from fetching competitor URLs to grading content with `grade_content`, runs in a dedicated, memory-safe environment that is destroyed after execution. Your data is never logged or stored.

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