How do I get a DataDome Management API Key?

Log in to your DataDome dashboard, navigate to **Management > API Keys**, and generate a new key. Ensure you use the management key for this integration.

Can the agent update bot rules?

This integration currently focuses on listing and auditing rules and threats. Updating or creating new bot rules should be handled through the DataDome dashboard.

What types of threats are tracked?

DataDome tracks various automated threats including scrapers, crawlers, account takeover attempts, and credential stuffing activities.

DataDome MCP Server for CrewAI 10 tools — connect in under 2 minutes

Name: DataDome
Availability: InStock
Author: Vinkius

docs.crewai.com/mcp/overview

Built by Vinkius GDPR 10 Tools Framework

Connect your CrewAI agents to DataDome through Vinkius, pass the Edge URL in the `mcps` parameter and every DataDome 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="DataDome Specialist",
    goal="Help users interact with DataDome effectively",
    backstory=(
        "You are an expert at leveraging DataDome 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 DataDome "
        "and summarize their capabilities."
    ),
    agent=agent,
    expected_output=(
        "A detailed summary of 10 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 DataDome MCP Server

Integrate DataDome, the leading bot protection and fraud prevention solution, directly into your AI workflow. Monitor your web and mobile applications for automated threats, audit protected endpoints, and track real-time protection statistics using natural language.

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

Threat Monitoring — List and retrieve full details for recently detected bot threats and suspicious activities.
Endpoint Auditing — Monitor the health and protection status of all your integrated web and mobile endpoints.
Protection Insights — Access real-time statistics and dashboard data on bot traffic and mitigation.
Rule Management — List custom bot rules and explore bypass tokens configured in your account.

The DataDome MCP Server exposes 10 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 DataDome to CrewAI via MCP

Follow these steps to integrate the DataDome 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 10 tools from DataDome

Why Use CrewAI with the DataDome MCP Server

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

DataDome + CrewAI Use Cases

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

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

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

DataDome MCP Tools for CrewAI (10)

These 10 tools become available when you connect DataDome to CrewAI via MCP:

get_bot_traffic_summary

Returns a summary of traffic from "Good Bots" (e.g., search engines) versus "Bad Bots" (e.g., scrapers, scrapers) and their impact on total traffic. Get a summary of bot traffic categorized by type

get_endpoint_health

Returns latency metrics, error rates, and the current operational state of the DataDome integration for the endpoint. Check the health status of a specific protected endpoint

get_protection_stats

Returns real-time counts of allowed vs. blocked requests, captcha pass rates, and identified bot categories. Retrieve real-time protection statistics and dashboard data

get_threat_details

Resolves detailed request headers, behavioral patterns, and the specific detection logic triggered. Get full technical details for a specific threat ID

list_access_logs

Returns a stream of recent requests processed by DataDome, including bot scores, decision outcomes, and geo-location data. List recent access logs filtered by DataDome

list_custom_bot_rules

Returns a list of custom bot detection rules, including match criteria (IP, User-Agent, etc.), action (allow/block/captcha), and rule priority. List all custom blocking and allowing rules

list_protected_applications

Returns application names, API keys (masked), and the types of protection enabled (Web/Mobile/API). List all applications (mobile, web) integrated with DataDome

list_protected_endpoints

Returns metadata including endpoint URLs, protection status, and associated application IDs. List all endpoints protected by DataDome

list_recent_threats

Returns a list of recent security incidents including threat types (e.g., scraping, credential stuffing), origin IPs, and detection timestamps. List recently detected bot threats and suspicious activities

search_threats_by_type

Matches the provided threat type against recent incidents to isolate specific attack vectors like "scraper" or "crawler". Search for recent threats by threat type keyword

Example Prompts for DataDome in CrewAI

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

"List all recent bot threats detected in the last 24 hours."

"Check the health status of our main 'Mobile App' endpoint."

"What is the summary of bot traffic for this week?"

Troubleshooting DataDome MCP Server with CrewAI

Common issues when connecting DataDome 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.

DataDome + CrewAI FAQ

Common questions about integrating DataDome 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.