Can I see the chart configurations for a specific Mode report through my agent?

Yes. Use the `get_report` tool with a specific Report Token. Your agent will retrieve the detailed analytical parameters, exposing the chart configurations and the underlying query logic used to generate the report data.

How do I check which databases are currently linked to my Mode workspace?

The `list_data_sources` tool retrieves all database and warehouse connectors bound to your account. Your agent will report the source names and mapped schemas, helping you identify which data is available for SQL reporting.

Can my agent help me find specific reports across different organizational spaces?

Absolutely. Use the `search_reports` tool with a search query. Your agent will filter through the metadata of all reports across your workspace, helping you locate critical data insights instantly without manual navigation.

Mode (Collaborative Data Platform) MCP Server for CrewAI 7 tools — connect in under 2 minutes

Name: Mode (Collaborative Data Platform)
Availability: InStock
Author: Vinkius

docs.crewai.com/mcp/overview

Built by Vinkius GDPR 7 Tools Framework

Connect your CrewAI agents to Mode (Collaborative Data Platform) through Vinkius, pass the Edge URL in the `mcps` parameter and every Mode (Collaborative Data Platform) tool is auto-discovered at runtime. No credentials to manage, no infrastructure to maintain.

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python

from crewai import Agent, Task, Crew

agent = Agent(
    role="Mode (Collaborative Data Platform) Specialist",
    goal="Help users interact with Mode (Collaborative Data Platform) effectively",
    backstory=(
        "You are an expert at leveraging Mode (Collaborative Data Platform) 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 Mode (Collaborative Data Platform) "
        "and summarize their capabilities."
    ),
    agent=agent,
    expected_output=(
        "A detailed summary of 7 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 Mode (Collaborative Data Platform) MCP Server

Connect your Mode Analytics account to any AI agent and take full control of your enterprise business intelligence, collaborative SQL reporting, and data source management through natural conversation.

When paired with CrewAI, Mode (Collaborative Data Platform) becomes a first-class tool in your multi-agent workflows. Each agent in the crew can call Mode (Collaborative Data Platform) 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

Report Orchestration — List all managed data reports and retrieve detailed analytical parameters, including chart configurations and query states directly from your agent
Space Navigation — Explore organizational 'Spaces' (Personal, Shared) to retrieve the exact report tokens needed to query scoped analytical boundaries natively
Global Analytics Search — Execute workspace-wide searches to identify specific reports and datasets matching literal metadata descriptions or keywords
Data Source Audit — Enumerate explicit database and warehouse connector sources bound to your Mode account to understand which schemas are available for querying
Member Tracking — List statically tracked analytical users within your workspace to verify report ownership and collaborative boundaries securely
Metadata Inspection — Deep-dive into specific Report or Space tokens to retrieve precise configuration details and chart definitions instantly

The Mode (Collaborative Data Platform) MCP Server exposes 7 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 Mode (Collaborative Data Platform) to CrewAI via MCP

Follow these steps to integrate the Mode (Collaborative Data Platform) 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 7 tools from Mode (Collaborative Data Platform)

Why Use CrewAI with the Mode (Collaborative Data Platform) MCP Server

CrewAI Multi-Agent Orchestration Framework provides unique advantages when paired with Mode (Collaborative Data Platform) 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

Mode (Collaborative Data Platform) + CrewAI Use Cases

Practical scenarios where CrewAI combined with the Mode (Collaborative Data Platform) MCP Server delivers measurable value.

Automated multi-step research: a reconnaissance agent queries Mode (Collaborative Data Platform) 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 Mode (Collaborative Data Platform), analyzes trends over time, and generates executive briefings in markdown or PDF format

Multi-source enrichment pipelines: chain Mode (Collaborative Data Platform) 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 Mode (Collaborative Data Platform) against predefined policy rules, generates deviation reports, and routes findings to the appropriate team

Mode (Collaborative Data Platform) MCP Tools for CrewAI (7)

These 7 tools become available when you connect Mode (Collaborative Data Platform) to CrewAI via MCP:

get_report

Get specific analytical parameters mapping a single tracked Mode report token

get_space

Get parameters mapping an explicitly targeted collection Space

list_data_sources

List explicit Database/Warehouse connector sources bound to Mode

list_members

List statically tracked analytical users joined within the workspace

list_reports

List static data reports generated by the Mode workspace

list_spaces

List accessible Spaces isolating datasets across the Mode workspace

search_reports

Search all reports evaluating queries natively against Mode API

Example Prompts for Mode (Collaborative Data Platform) in CrewAI

Ready-to-use prompts you can give your CrewAI agent to start working with Mode (Collaborative Data Platform) immediately.

"List all reports in my 'Shared' space"

"Search for any reports related to 'Marketing ROI' in the workspace"

"Show me the data sources currently connected to our Mode account"

Troubleshooting Mode (Collaborative Data Platform) MCP Server with CrewAI

Common issues when connecting Mode (Collaborative Data Platform) 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.

Mode (Collaborative Data Platform) + CrewAI FAQ

Common questions about integrating Mode (Collaborative Data Platform) 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.