How to Use the VectorShift (AI Workflow & RAG Automation) MCP in CrewAI

Q: What if I need to audit a running operation using VectorShift (AI Workflow & RAG Automation) with CrewAI?

You can use listpipelines and getpipeline to check the status of any ongoing workflow. This lets you monitor agent execution history and progress.

Q: Can I reset or change an agent's context using VectorShift (AI Workflow & RAG Automation) and CrewAI?

Yes. You can use deleteknowledgebase to wipe a knowledge base, forcing the agents to rebuild their context from scratch if needed.

Q: If I need to delete content, what tool do I use with VectorShift (AI Workflow & RAG Automation) and CrewAI?

You should use deleteknowledgebasedocuments for precise cleanup. This removes specific files by ID from a knowledge base without disturbing the container itself.

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Connect VectorShift (AI Workflow & RAG Automation) MCP to CrewAI

Create your Vinkius account to connect VectorShift (AI Workflow & RAG Automation) 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 VectorShift (AI Workflow & RAG Automation) with CrewAI

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Orchestrate agent collaboration via the MCP Server

The `create_pipeline` tool allows you to define a sequence of tasks that specialized agents must follow. You initiate this entire operation using `run_pipeline`. This structure is ideal for autonomous operations where Agent A hands off results to Agent B, which then executes the next step.

Maintain shared knowledge with CrewAI

Use `create_knowledge_base` and `index_knowledge_base` to load external data into a central source. Agents can then query this pool using `query_knowledge_base`. This ensures all agents are working from the same, up-to-date facts base.

Manage agent communication with MCP Server

You control conversational flow by creating a specialized chat bot using `create_chatbot`. Then, you run it with `run_chatbot` to get immediate responses. This allows one agent to act as the primary communicator or researcher for the entire crew.

Setup guide

Set up VectorShift (AI Workflow & RAG Automation) 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 VectorShift (AI Workflow & RAG Automation) tools as needed.

crew.py

from crewai import Agent, Task, Crew

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

task = Task(
    description="List recent VectorShift (AI Workflow & RAG Automation) 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="VectorShift (AI Workflow & RAG Automation) Analyst",
        goal="Access and analyze VectorShift (AI Workflow & RAG Automation) data via MCP.",
        backstory="Expert analyst with direct VectorShift (AI Workflow & RAG Automation) access.",
        tools=mcp_tools,
    )

    task = Task(
        description="List recent VectorShift (AI Workflow & RAG Automation) 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 VectorShift. 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 VectorShift (AI Workflow & RAG Automation) MCP in CrewAI

The MCP Server provides full lifecycle control. By managing pipelines (`create_pipeline`) and knowledge bases, you build the necessary backend structure for agents to operate without constant human input.

The server manages documents derived from files and URLs. This indexed knowledge base is the shared memory that your specialized crew agents read from to make decisions.

You can use `list_pipelines` and `get_pipeline` to check the status of any ongoing workflow. This lets you monitor agent execution history and progress.

Yes. You can use `delete_knowledge_base` to wipe a knowledge base, forcing the agents to rebuild their context from scratch if needed.

You should use `delete_knowledge_base_documents` for precise cleanup. This removes specific files by ID from a knowledge base without disturbing the container itself.

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