How to Use the Weaviate MCP in CrewAI

Q: How do I use Weaviate with CrewAI for vector search?

You assign a 'Researcher' agent the searchnearvector tool. This agent takes the query and returns results, which can then be passed to an 'Analyst' agent for interpretation.

Q: What is the best way to get the schema in CrewAI?

Have your 'Planner' agent call getfullschema at the start of the mission. This provides the entire context needed for all subsequent agents to operate correctly.

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Connect Weaviate MCP to CrewAI

Create your Vinkius account to connect Weaviate 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 Weaviate with CrewAI

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Coordinate Agents using the MCP Server

Assign specialized roles to agents. For example, one agent uses `get_class_schema` for planning, and a second agent executes `search_near_vector` based on that plan. The crew shares memory so Agent B knows what schema Agent A found.

Monitor System Status with Weaviate MCP Server

Assign a dedicated Monitor agent to check the cluster health using `get_cluster_nodes`. This ensures that every autonomous decision—from listing objects to getting details—is only attempted if the underlying infrastructure is confirmed operational.

Build Autonomous Operations with Weaviate MCP Server

A specialized Agent can be tasked solely with data retrieval. It uses `get_object_details` via a UUID, acting as the 'Data Retrieval' role. This allows for seamless execution of complex reads without human intervention.

Setup guide

Set up Weaviate 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 Weaviate tools as needed.

crew.py

from crewai import Agent, Task, Crew

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

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

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

You assign a 'Researcher' agent the `search_near_vector` tool. This agent takes the query and returns results, which can then be passed to an 'Analyst' agent for interpretation.

Yes. The framework lets you define multiple specialized agents. One agent might use `get_object_details`, while another uses `list_objects` to paginate through results for a comprehensive view.

Have your 'Planner' agent call `get_full_schema` at the start of the mission. This provides the entire context needed for all subsequent agents to operate correctly.

The MCP Server structure allows it to work directly. You pass the URL and tool definitions, letting your crew run autonomous operations against the database tools.

This server handles structured metadata and content associated with data objects. The agents read this information using `get_object_details` to perform their assigned tasks, keeping the process self-contained.

Use it with your favorite AI tools

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