How can I explore the hierarchy of my company's Nuclino configuration?

Your AI agent can progressively drill down by invoking `list_teams`, taking those IDs into `list_workspaces`, checking the clusters via `list_collections`, and finally dumping the granular content using `list_items`.

Can the agent perform global searches if I don't know the workspace?

Yes. Instead of manually parsing directories, ask the agent to invoke the `search_items` tool. It queries all permissible areas on Nuclino simultaneously and returns exact contextual matches within seconds.

Can I automatically append meeting notes as a brand new document?

Absolutely. Once an AI process finishes an important chat, use the `create_item` tool to generate a fresh target URL holding the transcribed content straight into any specified workspace.

Nuclino MCP Server for CrewAI 12 tools — connect in under 2 minutes

Name: Nuclino
Availability: InStock
Author: Vinkius

docs.crewai.com/mcp/overview

Built by Vinkius GDPR 12 Tools Framework

Connect your CrewAI agents to Nuclino through Vinkius, pass the Edge URL in the `mcps` parameter and every Nuclino 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="Nuclino Specialist",
    goal="Help users interact with Nuclino effectively",
    backstory=(
        "You are an expert at leveraging Nuclino 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 Nuclino "
        "and summarize their capabilities."
    ),
    agent=agent,
    expected_output=(
        "A detailed summary of 12 available tools "
        "and what they can do."
    ),
)

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

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* 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 Nuclino MCP Server

Connect your Nuclino account to your AI agent and seamlessly interact with your company's unified workspace for knowledge, docs, and projects.

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

Teams & Workspaces — Rapidly list all teams you are part of, and enumerate the nested workspaces and collections to understand your organization's hierarchy.
Search & Query — Perform global fuzzy searches using search_items to track down specific documents, notes, or project pages across the entire knowledge base.
Read Items & Files — Read the exact content configuration of any item (document) via get_item, and list attachments or files uploaded to the platform.
Record Creation — Instantly create new items natively inside your workspace using natural language.
Telemetrics — Enumerate members and structural fields within your Nuclino domain to keep the agent aware of context and owners.

The Nuclino MCP Server exposes 12 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 Nuclino to CrewAI via MCP

Follow these steps to integrate the Nuclino 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 12 tools from Nuclino

Why Use CrewAI with the Nuclino MCP Server

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

Nuclino + CrewAI Use Cases

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

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

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

Nuclino MCP Tools for CrewAI (12)

These 12 tools become available when you connect Nuclino to CrewAI via MCP:

create_item

Triggers real-time replication creating permanent Wiki documentation. Write a brand new knowledge Item / Page into a Workspace

delete_item

Always confirm with the user heavily before destroying knowledge. Irreversibly delete a structural Nuclino Item

get_item

Retrieve the exact Markdown payload and configuration of an Item

list_collections

Used to trace the document relationship graph paths visually within a target Workspace. List Collections (grouping directories) segmenting a Workspace

list_fields

Used to understand standard taxonomy dimensions applicable against Items. Map customizable structured property fields globally binding a Team

list_files

Exposes pure URL bindings mapping binary data records back to object storage. List physical attachments explicitly bolted onto an Item

list_items

Used to enumerate top-level document UUIDs, titles, and creation metadata natively spanning a specific Workspace layer. List all standard knowledge items (pages) in a Workspace

list_teams

Use this as the entry point to discover available root organizational unit IDs traversing down into workspaces. List all organizational Teams the authenticated user belongs to

list_users

Enumerate human identities attached globally onto a Team

list_workspaces

Returns internal workspace UUIDs essential for scoping later item queries. List all isolated Workspaces mapped within a specific Team

search_items

Use to uncover unknown UUIDs. Execute an indexed semantic search globally across a Team

update_item

Alters the sync tree immediately appending new wiki edits. Overwrite active partial Markdown states inside a listed Item

Example Prompts for Nuclino in CrewAI

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

"Search Nuclino for any documentation mentioning 'SSO Security Policies'."

"Create an item titled 'Project X Architecture Brief' in the Engineering workspace."

"List all teams connected to this authentication token."

Troubleshooting Nuclino MCP Server with CrewAI

Common issues when connecting Nuclino 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.

Nuclino + CrewAI FAQ

Common questions about integrating Nuclino 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.