CrewAIFramework

Onfido MCP Server

Bring Identity Verification
to CrewAI

Name: Onfido MCP Server
Price: 29 USD
Availability: InStock
Rating: 5.0 (1 reviews)
Author: Vinkius

Learn how to connect Onfido to CrewAI and start using 6 AI agent tools in minutes. Fully managed, enterprise secure, and ready to use without writing a single line of code.

GDPR Free for Subscribers

Create ApplicantCreate CheckCreate Workflow RunGet ReportGet Workflow RunRegister Webhook

Unlock for AI Agents

Ask AI about this MCP Server ChatGPT Claude Perplexity

Compatible with every major AI agent and IDE

Claude

ChatGPT

Cursor

Gemini

Windsurf

VS Code

JetBrains

Vercel

+ other MCP clients

What is the Onfido MCP Server?

Connect your Onfido account to any AI agent to streamline your KYC (Know Your Customer) and identity verification processes through natural conversation.

What you can do

Applicant Management — Create and manage applicant profiles representing individuals undergoing verification.
Workflow Studio Integration — Start and monitor complex verification workflows (Workflow Runs) to automate multi-step identity checks.
Legacy Checks — Create classic checks for specific report combinations like document verification and facial similarity.
Detailed Reporting — Retrieve comprehensive verification reports, including results and granular breakdowns of identity data.
Real-time Notifications — Register webhooks to receive asynchronous updates on verification statuses directly to your systems.

How it works

Subscribe to this server
Enter your Onfido API Token
Start managing identity verifications from Claude, Cursor, or any MCP-compatible client

Who is this for?

Compliance Officers — instantly check the status of an applicant's verification or review report breakdowns without manual dashboard searching.
Fintech Developers — integrate and test identity verification flows directly from the code editor or terminal.
Operations Teams — automate the creation of applicants and trigger verification workflows as part of user onboarding.

Built-in capabilities (6)

create_applicant

Create a new Onfido applicant

create_check

Create a new Check (Legacy/Classic)

create_workflow_run

Create a new Workflow Run

get_report

Retrieve a Verification Report

get_workflow_run

g., awaiting_input, processing, approved, review, declined). Retrieve a Workflow Run

register_webhook

Why CrewAI?

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

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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
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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
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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
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Sequential and hierarchical crew patterns map naturally to real-world workflows: enumerate subdomains → analyze DNS history → check WHOIS records → compile findings into actionable reports

See it in action

Onfido in CrewAI

AI Agent→Vinkius

High Security·Kill Switch·Plug and Play

Why Vinkius

Onfido and 4,000+ other MCP servers. One platform. One governance layer.

Teams that connect Onfido to CrewAI through Vinkius don't need to source, host, or maintain individual MCP servers. Every tool call runs inside a hardened runtime with credential isolation, DLP, and a signed audit chain.

4,000+MCP Servers ready

<40msCold start

60%Token savings

	Raw MCP	Vinkius
Server catalog	Find and host yourself	4,000+ managed
Infrastructure	Self-hosted	Sandboxed V8 isolates
Credential handling	Plaintext in config	Vault + runtime injection
Data loss prevention	None	Configurable DLP policies
Kill switch	None	Global instant shutdown
Financial circuit breakers	None	Per-server limits + alerts
Audit trail	None	Ed25519 signed logs
SIEM log streaming	None	Splunk, Datadog, Webhook
Honeytokens	None	Canary alerts on leak
Custom domains	Not applicable	DNS challenge verified
GDPR compliance	Manual effort	Automated purge + export

Unlock for AI Agents View step-by-step setup guide for CrewAI →

Enterprise Security

Why teams choose Vinkius for Onfido in CrewAI

The Onfido 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. All 6 tools execute in hardened sandboxes optimized for native MCP execution.

Your AI agents in CrewAI only access the data you authorize, with DLP that blocks sensitive information from ever reaching the model, kill switch for instant shutdown, and up to 60% token savings. Enterprise-grade infrastructure, zero maintenance.

Unlock for AI Agents View full Onfido details →

Fully ManagedVinkius Servers

60%Token savings

High SecurityEnterprise-grade

IAMAccess control

EU AI ActCompliant

DLPData protection

V8 IsolateSandboxed

Ed25519Audit chain

<40msKill switch

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

The Vinkius Advantage

How Vinkius secures
Onfido for CrewAI

Every tool call from CrewAI to the Onfido MCP Server is protected by DLP redaction, cryptographic audit chains, V8 sandbox isolation, kill switch, and financial circuit breakers.

< 40msCold start

Ed25519Signed audit chain

60%Token savings

FAQ

Frequently asked questions

How can I check the current status of a verification process?

Use the get_workflow_run tool with the specific Workflow Run ID. It will return the current state, such as 'awaiting_input', 'processing', 'approved', or 'declined'.

Can I create a verification check without using Workflow Studio?

Yes. Use the create_check tool for legacy integrations. You can specify a comma-separated list of report names, such as 'document, facial_similarity_photo', for a specific applicant ID.

How do I get notified when a report is completed?

You can use the register_webhook tool to set up a listener URL for specific events like 'report.completed', 'check.completed', or 'workflow_run.completed'.

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.