Can I see if a client has finished their onboarding dossier?

Yes! Use the `get_application_status_details` tool with the dossier ID. The agent will return the current progress percentage and list any pending tasks or documents.

How do I invite a new client to start an application?

First, use `launch_new_onboarding` to create the dossier for the client. Then, use the `send_onboarding_invitation` tool to trigger the welcome email with their portal link.

Where do I find my Clustdoc API Token?

Log in to Clustdoc, navigate to **Team Settings**, and select **Developer**. You can generate and copy your API token from the API section.

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

Name: Clustdoc
Availability: InStock
Author: Vinkius

docs.crewai.com/mcp/overview

Built by Vinkius GDPR 8 Tools Framework

Connect your CrewAI agents to Clustdoc through the Vinkius — pass the Edge URL in the `mcps` parameter and every Clustdoc 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

Open in ChatGPT Open in Claude Open in Perplexity

Vinkius supports streamable HTTP and SSE.

python

from crewai import Agent, Task, Crew

agent = Agent(
    role="Clustdoc Specialist",
    goal="Help users interact with Clustdoc effectively",
    backstory=(
        "You are an expert at leveraging Clustdoc 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 Clustdoc "
        "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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* 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 Clustdoc MCP Server

Connect your Clustdoc account to any AI agent and take full control of your client onboarding and document collection through natural conversation. Streamline how you manage complex applications and workflows natively.

When paired with CrewAI, Clustdoc becomes a first-class tool in your multi-agent workflows. Each agent in the crew can call Clustdoc tools autonomously — one agent queries data, another analyzes results, a third compiles reports — all orchestrated through the Vinkius with zero configuration overhead.

What you can do

Template Oversight — List and retrieve details for all onboarding workflow templates configured in your account natively
Dossier Intelligence — Access and monitor individual client applications (dossiers) and their current progress flawlessly
Application Lifecycle — Launch new onboarding sessions for clients using pre-defined templates securely
Invitation Logistics — Trigger automated portal invitation emails to clients directly from your chat interface flawlessly
Team Management — List all teams and members within your Clustdoc account to manage access flawlessly
integrated Visibility — Retrieve detailed application metadata including status and contact information directly within your workspace

The Clustdoc 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 Clustdoc to CrewAI via MCP

Follow these steps to integrate the Clustdoc 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 Clustdoc

Why Use CrewAI with the Clustdoc MCP Server

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

Clustdoc + CrewAI Use Cases

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

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

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

Clustdoc MCP Tools for CrewAI (8)

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

get_application_status_details

Get detailed status and progress for a specific client dossier

get_my_clustdoc_profile

Retrieve information about the authenticated user

get_workflow_configuration

Get detailed configuration for a specific onboarding template

launch_new_onboarding

Launch a new onboarding application for a client

list_client_dossiers

List all active and completed client applications (dossiers)

list_clustdoc_teams

List all teams and members in the Clustdoc account

list_onboarding_templates

List all onboarding workflow templates

send_onboarding_invitation

Send the portal invitation email to the client for a specific dossier

Example Prompts for Clustdoc in CrewAI

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

"List all active client dossiers in Clustdoc."

"Launch a new 'Standard Business Onboarding' for john@example.com."

"What is the status of the dossier for 'TechFlow Inc'?"

Troubleshooting Clustdoc MCP Server with CrewAI

Common issues when connecting Clustdoc 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

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

Clustdoc + CrewAI FAQ

Common questions about integrating Clustdoc 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.