How do I generate an API token in Quantive?

Log in to your Quantive account, navigate to **Setup > Configuration > API tokens**, and click 'Generate token'. You'll need to select a user for the token's permissions.

Which data center should I select?

Check your browser URL when logged in. If it says `app.us.quantive.com`, select **US**. If it says `app.eu.quantive.com`, select **EU**.

Can I update the progress of my Key Results through this integration?

Yes! Use the `update_key_result` tool to set the current numeric value of any KR, and Quantive will automatically calculate the attainment percentage.

Does this integration support multiple sessions?

Yes. You can list all active and past sessions using `list_sessions`, allowing you to query OKRs from different timeframes as needed.

Quantive (Gtmhub) MCP Server for CrewAI 10 tools — connect in under 2 minutes

Name: Quantive (Gtmhub)
Availability: InStock
Author: Vinkius

docs.crewai.com/mcp/overview

Built by Vinkius GDPR 10 Tools Framework

Connect your CrewAI agents to Quantive (Gtmhub) through the Vinkius — pass the Edge URL in the `mcps` parameter and every Quantive (Gtmhub) 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

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Vinkius supports streamable HTTP and SSE.

python

from crewai import Agent, Task, Crew

agent = Agent(
    role="Quantive (Gtmhub) Specialist",
    goal="Help users interact with Quantive (Gtmhub) effectively",
    backstory=(
        "You are an expert at leveraging Quantive (Gtmhub) 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 Quantive (Gtmhub) "
        "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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About Quantive (Gtmhub) MCP Server

Connect your Quantive (formerly Gtmhub) strategy platform to any AI agent and drive your organizational goals through natural conversation.

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

Objective Tracking — List and inspect strategic objectives to align your team's focus.
Key Result Management — Monitor progress on KRs and update current values directly from your chat or IDE.
Session Overview — Browse planning sessions and timeframes to understand quarterly or annual goals.
Team & User Insights — Retrieve team structures and user profiles to facilitate better collaboration.
Task Execution — List tasks linked to specific OKRs to bridge the gap between strategy and execution.

The Quantive (Gtmhub) 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 Quantive (Gtmhub) to CrewAI via MCP

Follow these steps to integrate the Quantive (Gtmhub) 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 Quantive (Gtmhub)

Why Use CrewAI with the Quantive (Gtmhub) MCP Server

CrewAI Multi-Agent Orchestration Framework provides unique advantages when paired with Quantive (Gtmhub) 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

Quantive (Gtmhub) + CrewAI Use Cases

Practical scenarios where CrewAI combined with the Quantive (Gtmhub) MCP Server delivers measurable value.

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

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

Quantive (Gtmhub) MCP Tools for CrewAI (10)

These 10 tools become available when you connect Quantive (Gtmhub) to CrewAI via MCP:

get_key_result

Get details for a specific key result

get_my_profile

Get information about the current authenticated user

get_objective

Get details for a specific OKR objective

list_key_results

List all key results (metrics) in Quantive

list_objectives

List all OKR objectives in Quantive (Gtmhub)

list_sessions

g., Q1, Annual) used to group OKRs. List all planning sessions (timeframes) in Quantive

list_tasks

List tasks associated with OKRs

list_teams

List all organizational teams

list_users

List user profiles in the Quantive account

update_key_result

Update the current value of a key result

Example Prompts for Quantive (Gtmhub) in CrewAI

Ready-to-use prompts you can give your CrewAI agent to start working with Quantive (Gtmhub) immediately.

"What are our main objectives for the current session?"

"Update key result ID 593021 to 75."

"List all teams assigned to our strategic objectives."

Troubleshooting Quantive (Gtmhub) MCP Server with CrewAI

Common issues when connecting Quantive (Gtmhub) 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.

Quantive (Gtmhub) + CrewAI FAQ

Common questions about integrating Quantive (Gtmhub) 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.