How to Use the Verba MCP in CrewAI

Q: How do I get answers from Verba using CrewAI?

Assign an agent to call performragquery. That agent retrieves summarized, cited answers from the Verba knowledge base and passes them along for team analysis.

Q: Can CrewAI add documents to Verba?

Yes. An agent can execute addknowledgedocument to ingest new content alongside metadata into the Verba knowledge base, keeping your collaborative data set fresh.

Q: How do I list documents in Verba with CrewAI?

An agent simply calls listknowledgedocuments. It pulls a complete inventory of every document indexed, giving the entire team visibility into what knowledge is available.

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Connect Verba MCP to CrewAI

Create your Vinkius account to connect Verba 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 Verba with CrewAI

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Run specialized research queries

The `perform_rag_query` tool lets your agent team search the Verba documents. One agent can pull summarized answers and citations, passing verified facts to another for analysis. This mechanism allows autonomous operations to rely on a single source of truth—your knowledge base—making decision-making reliable.

Control document lifecycle

`add_knowledge_document` lets an agent ingest new content and metadata into the Verba knowledge base. A dedicated research agent can automatically add findings to keep the team’s data current. If a source is proven wrong, another specialized agent can call `delete_knowledge_document` to permanently remove it from the index.

Check system parameters

`get_system_config` allows your crew of agents to check Verba’s current operational settings. This is crucial for a moderator agent that needs to know if an action is permitted. Agents can also use `list_knowledge_documents` to quickly verify the scope and availability of all indexed knowledge.

Setup guide

Set up Verba 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 Verba tools as needed.

crew.py

from crewai import Agent, Task, Crew

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

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

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

Assign an agent to call `perform_rag_query`. That agent retrieves summarized, cited answers from the Verba knowledge base and passes them along for team analysis.

Yes. An agent can execute `add_knowledge_document` to ingest new content alongside metadata into the Verba knowledge base, keeping your collaborative data set fresh.

The server deals with structured text: document content and associated metadata. Your autonomous operations rely on correctly managing these types when reading or writing to Verba's index.

An agent simply calls `list_knowledge_documents`. It pulls a complete inventory of every document indexed, giving the entire team visibility into what knowledge is available.

The server manages structured text types: document content and metadata. This means all agents operate on these specific, verifiable information payloads to run their tasks.

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