How to Use the Vald MCP in CrewAI

Q: How do agents find information using Vald and CrewAI?

Agents use searchvectors to query the database, performing a nearest neighbor search on your knowledge vectors. The best matches are then passed into the agent's context for analysis.

Q: Can I check the health of the Vald MCP Server with CrewAI?

Yes, use getengineinfo to retrieve operational information and health. This confirms that the entire autonomous operation can proceed without database hiccups.

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Connect Vald MCP to CrewAI

Create your Vinkius account to connect Vald 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 Vald with CrewAI

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Specialized Research using the MCP Server

Designate an agent to research context by running `search_vectors`. This performs a nearest neighbor similarity search across your knowledge base. The resulting vectors provide specialized, actionable information for subsequent analysis agents.

Maintaining Memory in CrewAI Teams

If one agent needs to modify shared context, it calls `update_vector` with the existing ID and new vector array. This acts like shared memory, ensuring all specialized agents work from the latest dataset.

Starting New Knowledge Bases in CrewAI

When a new topic comes up that needs tracking, use `insert_vector`. Providing a unique ID and the initial vector array allows your crew to build out its knowledge base from scratch. This is key for autonomous operations.

Setup guide

Set up Vald 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 Vald tools as needed.

crew.py

from crewai import Agent, Task, Crew

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

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

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

Agents use `search_vectors` to query the database, performing a nearest neighbor search on your knowledge vectors. The best matches are then passed into the agent's context for analysis.

Yes, use `get_engine_info` to retrieve operational information and health. This confirms that the entire autonomous operation can proceed without database hiccups.

It manages dense vectors using JSON arrays of floats. The core data type you're dealing with is vector data, which the MCP Server indexes and retrieves for your multi-agent teams.

It’s built for scale. The ability to insert, retrieve, and update vectors makes it perfect for maintaining shared memory across multiple specialized agents.

The server handles vector data. This involves managing both the raw float arrays and the unique identifiers associated with those vectors in your autonomous operations.

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