How do I get a DISQO API Key?

Log in to your DISQO platform account, navigate to the API section in your organization settings, and you can generate or retrieve your unique API Key from there. Note that API access may require a specific plan.

Can the agent show real-time consumer behavior?

Yes, you can use the list_behavioral_metrics tool to retrieve available consumer data points and high-level trends as reported by the DISQO behavioral data platform.

Is the integration secure for sensitive research data?

Absolutely. This integration uses official DISCO API protocols and only accesses the projects and insights exposed via your API key permissions, ensuring enterprise-grade research security.

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

Name: DISQO
Availability: InStock
Author: Vinkius

docs.crewai.com/mcp/overview

Built by Vinkius GDPR 10 Tools Framework

Connect your CrewAI agents to DISQO through Vinkius, pass the Edge URL in the `mcps` parameter and every DISQO 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="DISQO Specialist",
    goal="Help users interact with DISQO effectively",
    backstory=(
        "You are an expert at leveraging DISQO 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 DISQO "
        "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)

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

About DISQO MCP Server

Integrate DISQO, the leading consumer insights and behavioral data platform, directly into your AI workflow. Manage your research projects, monitor real-time consumer trends and behavioral metrics, and track your audience panels and surveys using natural language.

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

Research Oversight — List and retrieve detailed settings and execution statuses for all your consumer insight projects.
Behavioral Intelligence — Access available behavioral metrics and data points tracked by the DISQO platform.
Audience Management — Monitor defined research audiences, including demographic filters and panel sizes.
Insight Tracking — Retrieve processed consumer insights and performance reports directly via chat.

The DISQO 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 DISQO to CrewAI via MCP

Follow these steps to integrate the DISQO 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 DISQO

Why Use CrewAI with the DISQO MCP Server

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

DISQO + CrewAI Use Cases

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

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

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

DISQO MCP Tools for CrewAI (10)

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

get_platform_metadata

Retrieve metadata and usage limits for your DISQO account

get_project_details

Get detailed settings and status for a specific DISQO project

list_behavioral_metrics

List behavioral metrics and data points tracked by DISQO

list_consumer_insights

List available consumer insights and behavioral reports

list_insight_projects

List all consumer insight projects in your DISQO account

list_largest_research_panels

Identify audience segments with the highest number of available panelists

list_research_audiences

List all defined consumer audiences available for research

list_running_research_projects

Identify research projects that are currently in the data collection phase

quick_behavioral_audit

Retrieve a high-level summary of the most active behavioral metrics

search_insights_by_keyword

Search for specific consumer insights or reports using a keyword

Example Prompts for DISQO in CrewAI

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

"List all active research projects."

"Show me the top behavioral metrics being tracked."

"Which research audience has the largest panel size?"

Troubleshooting DISQO MCP Server with CrewAI

Common issues when connecting DISQO 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.

DISQO + CrewAI FAQ

Common questions about integrating DISQO 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.