How do I find my List ID?

Use the `list_workspaces` tool to see all your workspaces and the lists they contain along with their unique IDs.

Can I filter entries in a list?

The current `list_entries` tool retrieves all items. For complex filtering, you can provide field-specific values when using the create or update tools.

Is it possible to see the field definitions of a list?

Absolutely. Use the `list_elements` tool with a list ID to retrieve all field names, types, and configurations.

Zenkit MCP Server for CrewAI 8 tools — connect in under 2 minutes

Name: Zenkit
Availability: InStock
Author: Vinkius

docs.crewai.com/mcp/overview

Built by Vinkius GDPR 8 Tools Framework

Connect your CrewAI agents to Zenkit through Vinkius, pass the Edge URL in the `mcps` parameter and every Zenkit tool is auto-discovered at runtime. No credentials to manage, no infrastructure to maintain.

Get MCP Server for AI Agents

ASK AI ABOUT THIS MCP SERVER

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Vinkius supports streamable HTTP and SSE.

python

from crewai import Agent, Task, Crew

agent = Agent(
    role="Zenkit Specialist",
    goal="Help users interact with Zenkit effectively",
    backstory=(
        "You are an expert at leveraging Zenkit 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 Zenkit "
        "and summarize their capabilities."
    ),
    agent=agent,
    expected_output=(
        "A detailed summary of 8 available tools "
        "and what they can do."
    ),
)

crew = Crew(agents=[agent], tasks=[task])
result = crew.kickoff()
print(result)

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Stream every event to Splunk, Datadog, or your own webhook in real-time

* 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 Zenkit MCP Server

Connect your Zenkit account to any AI agent to streamline your productivity and project management. This MCP server enables your agent to interact with workspaces, lists (collections), and data entries directly from natural language.

When paired with CrewAI, Zenkit becomes a first-class tool in your multi-agent workflows. Each agent in the crew can call Zenkit 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

Workspace Oversight — List all workspaces and retrieve their constituent lists and metadata
List Management — Query detailed configurations and field elements for any Zenkit list
Data Operations — List, retrieve, create, and update entries (items) within your collections
Field Discovery — Inspect list elements to understand the data structure and field types
Content Cleanup — Delete entries and maintain your lists directly via natural language commands

The Zenkit MCP Server exposes 8 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 Zenkit to CrewAI via MCP

Follow these steps to integrate the Zenkit 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 8 tools from Zenkit

Why Use CrewAI with the Zenkit MCP Server

CrewAI Multi-Agent Orchestration Framework provides unique advantages when paired with Zenkit 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

Zenkit + CrewAI Use Cases

Practical scenarios where CrewAI combined with the Zenkit MCP Server delivers measurable value.

Automated multi-step research: a reconnaissance agent queries Zenkit 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 Zenkit, analyzes trends over time, and generates executive briefings in markdown or PDF format

Multi-source enrichment pipelines: chain Zenkit 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 Zenkit against predefined policy rules, generates deviation reports, and routes findings to the appropriate team

Zenkit MCP Tools for CrewAI (8)

These 8 tools become available when you connect Zenkit to CrewAI via MCP:

create_entry

Requires a JSON object with field values. Create a new entry in a list

delete_entry

Delete an entry from a list

get_list_details

Get details for a specific list

get_workspace_details

Get details for a specific workspace

list_elements

List all elements (fields) defined in a list

list_entries

List all entries (items) in a list

list_workspaces

List all workspaces and their lists

update_entry

Update an existing entry

Example Prompts for Zenkit in CrewAI

Ready-to-use prompts you can give your CrewAI agent to start working with Zenkit immediately.

"List all my Zenkit workspaces and their collections."

"Show me all entries in the list with ID '98765'."

"Create a new entry in list '98765' with name 'Finish API documentation'."

Troubleshooting Zenkit MCP Server with CrewAI

Common issues when connecting Zenkit to CrewAI through the Vinkius, and how to resolve them.

MCP tools not discovered

Ensure the Edge URL is correct. CrewAI connects lazily when the crew starts. check console output.

Agent not using tools

Make the task description specific. Instead of "do something", say "Use the available tools to list contacts".

Timeout errors

CrewAI has a 10s connection timeout by default. Ensure your network can reach the Edge URL.

Rate limiting or 429 errors

Vinkius enforces per-token rate limits. Check your subscription tier and request quota in the dashboard. Upgrade if you need higher throughput.

Zenkit + CrewAI FAQ

Common questions about integrating Zenkit MCP Server with CrewAI.

How does CrewAI discover and connect to MCP tools?

CrewAI connects to MCP servers lazily. when the crew starts, each agent resolves its MCP URLs and fetches the tool catalog via the standard 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?

Yes. Each agent has its own 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?

CrewAI wraps tool failures as context for the agent. The LLM receives the error message and can decide to retry with different parameters, fall back to a different tool, or mark the task as partially complete. This resilience is critical for production workflows.

Can CrewAI agents call multiple MCP tools in parallel?

CrewAI agents execute tool calls sequentially within a single reasoning step. However, you can run multiple agents in parallel using 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)?

Yes. CrewAI crews are standard Python scripts, so you can invoke them via cron, Airflow, Celery, or any task scheduler. The crew.kickoff() method runs synchronously by default, making it straightforward to integrate into existing pipelines.