Verba MCP Server for CrewAI 6 tools — connect in under 2 minutes
Connect your CrewAI agents to Verba through Vinkius, pass the Edge URL in the `mcps` parameter and every Verba tool is auto-discovered at runtime. No credentials to manage, no infrastructure to maintain.
ASK AI ABOUT THIS MCP SERVER
Vinkius supports streamable HTTP and SSE.
from crewai import Agent, Task, Crew
agent = Agent(
role="Verba Specialist",
goal="Help users interact with Verba effectively",
backstory=(
"You are an expert at leveraging Verba tools "
"for automation and data analysis."
),
# Your Vinkius token. get it at cloud.vinkius.com
mcps=["https://edge.vinkius.com/[YOUR_TOKEN_HERE]/mcp"],
)
task = Task(
description=(
"Explore all available tools in Verba "
"and summarize their capabilities."
),
agent=agent,
expected_output=(
"A detailed summary of 6 available tools "
"and what they can do."
),
)
crew = Crew(agents=[agent], tasks=[task])
result = crew.kickoff()
print(result)
* Every MCP server runs on Vinkius-managed infrastructure inside AWS - a purpose-built runtime with per-request V8 isolates, Ed25519 signed audit chains, and sub-40ms cold starts optimized for native MCP execution. See our infrastructure
About Verba MCP Server
Intertwine the open-source Verba (by Weaviate) ecosystem natively into your conversational AI IDE. Execute powerful Retrieval-Augmented Generation processes and manage your localized knowledge bases simply by chatting.
When paired with CrewAI, Verba becomes a first-class tool in your multi-agent workflows. Each agent in the crew can call Verba tools autonomously, one agent queries data, another analyzes results, a third compiles reports, all orchestrated through Vinkius with zero configuration overhead.
What you can do
- Augmented Queries — Cast a question to your agent and have it retrieve fully synthesized answers from the Verba engine completely backed up by exact document citations.
- Knowledge Management — Insert new context text, list all ingested documents, retrieve the deeply embedded raw data of any ID, or remove dead knowledge dynamically without Web UIs.
- Health Checks — Request system configurations directly via chat to ensure your local LLM connections, embedding models, and cluster health are firing effectively.
The Verba MCP Server exposes 6 tools through the Vinkius. Connect it to CrewAI in under two minutes — no API keys to rotate, no infrastructure to provision, no vendor lock-in. Your configuration, your data, your control.
How to Connect Verba to CrewAI via MCP
Follow these steps to integrate the Verba MCP Server with CrewAI.
Install CrewAI
Run pip install crewai
Replace the token
Replace [YOUR_TOKEN_HERE] with your Vinkius token from cloud.vinkius.com
Customize the agent
Adjust the role, goal, and backstory to fit your use case
Run the crew
Run python crew.py. CrewAI auto-discovers 6 tools from Verba
Why Use CrewAI with the Verba MCP Server
CrewAI Multi-Agent Orchestration Framework provides unique advantages when paired with Verba through the Model Context Protocol.
Multi-agent collaboration lets you decompose complex workflows into specialized roles, one agent researches, another analyzes, a third generates reports, each with access to MCP tools
CrewAI's native MCP integration requires zero adapter code: pass Vinkius Edge URL directly in the `mcps` parameter and agents auto-discover every available tool at runtime
Built-in task delegation and shared memory mean agents can pass context between steps without manual state management, enabling multi-hop reasoning across tool calls
Sequential and hierarchical crew patterns map naturally to real-world workflows: enumerate subdomains → analyze DNS history → check WHOIS records → compile findings into actionable reports
Verba + CrewAI Use Cases
Practical scenarios where CrewAI combined with the Verba MCP Server delivers measurable value.
Automated multi-step research: a reconnaissance agent queries Verba for raw data, then a second analyst agent cross-references findings and flags anomalies. all without human handoff
Scheduled intelligence reports: set up a crew that periodically queries Verba, analyzes trends over time, and generates executive briefings in markdown or PDF format
Multi-source enrichment pipelines: chain Verba tools with other MCP servers in the same crew, letting agents correlate data across multiple providers in a single workflow
Compliance and audit automation: a compliance agent queries Verba against predefined policy rules, generates deviation reports, and routes findings to the appropriate team
Verba MCP Tools for CrewAI (6)
These 6 tools become available when you connect Verba to CrewAI via MCP:
add_knowledge_document
Provide the document content and optional metadata JSON. Ingests a new document into the Verba knowledge base
delete_knowledge_document
This action is irreversible. Permanently removes a document from the knowledge base
get_document_details
Retrieves the full content and metadata of a specific document
get_system_config
Retrieves the current Verba system configuration
list_knowledge_documents
Lists all documents indexed in the Verba knowledge base
perform_rag_query
Returns summarized answers with citations. Executes a RAG (Retrieval Augmented Generation) query against the Verba knowledge base
Example Prompts for Verba in CrewAI
Ready-to-use prompts you can give your CrewAI agent to start working with Verba immediately.
"Check Verba's configuration to see which embedding model it is currently using."
"Perform a RAG query asking: 'What are our key deployment steps based on the infrastructure guide?'"
"List all documents and output the unique ID of the 'Employee Code of Conduct' file."
Troubleshooting Verba MCP Server with CrewAI
Common issues when connecting Verba to CrewAI through the Vinkius, and how to resolve them.
MCP tools not discovered
Agent not using tools
Timeout errors
Rate limiting or 429 errors
Verba + CrewAI FAQ
Common questions about integrating Verba MCP Server with CrewAI.
How does CrewAI discover and connect to MCP tools?
tools/list method. This means tools are always fresh and reflect the server's current capabilities. No tool schemas need to be hardcoded.Can different agents in the same crew use different MCP servers?
mcps list, so you can assign specific servers to specific roles. For example, a reconnaissance agent might use a domain intelligence server while an analysis agent uses a vulnerability database server.What happens when an MCP tool call fails during a crew run?
Can CrewAI agents call multiple MCP tools in parallel?
process=Process.parallel, each calling different MCP tools concurrently. This is ideal for workflows where separate data sources need to be queried simultaneously.Can I run CrewAI crews on a schedule (cron)?
crew.kickoff() method runs synchronously by default, making it straightforward to integrate into existing pipelines.Connect Verba with your favorite client
Step-by-step setup guides for every MCP-compatible client and framework:
Anthropic's native desktop app for Claude with built-in MCP support.
AI-first code editor with integrated LLM-powered coding assistance.
GitHub Copilot in VS Code with Agent mode and MCP support.
Purpose-built IDE for agentic AI coding workflows.
Autonomous AI coding agent that runs inside VS Code.
Anthropic's agentic CLI for terminal-first development.
Python SDK for building production-grade OpenAI agent workflows.
Google's framework for building production AI agents.
Type-safe agent development for Python with first-class MCP support.
TypeScript toolkit for building AI-powered web applications.
TypeScript-native agent framework for modern web stacks.
Python framework for orchestrating collaborative AI agent crews.
Leading Python framework for composable LLM applications.
Data-aware AI agent framework for structured and unstructured sources.
Microsoft's framework for multi-agent collaborative conversations.
Connect Verba to CrewAI
Get your token, paste the configuration, and start using 6 tools in under 2 minutes. No API key management needed.