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 Pydantic AI 10 tools — connect in under 2 minutes

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

ai.pydantic.dev/mcp

Built by Vinkius GDPR 10 Tools SDK

Pydantic AI brings type-safe agent development to Python with first-class MCP support. Connect Quantive (Gtmhub) through Vinkius and every tool is automatically validated against Pydantic schemas. catch errors at build time, not in production.

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

import asyncio
from pydantic_ai import Agent
from pydantic_ai.mcp import MCPServerHTTP

async def main():
    # Your Vinkius token. get it at cloud.vinkius.com
    server = MCPServerHTTP(url="https://edge.vinkius.com/[YOUR_TOKEN_HERE]/mcp")

    agent = Agent(
        model="openai:gpt-4o",
        mcp_servers=[server],
        system_prompt=(
            "You are an assistant with access to Quantive (Gtmhub) "
            "(10 tools)."
        ),
    )

    result = await agent.run(
        "What tools are available in Quantive (Gtmhub)?"
    )
    print(result.data)

asyncio.run(main())

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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.

Pydantic AI validates every Quantive (Gtmhub) tool response against typed schemas, catching data inconsistencies at build time. Connect 10 tools through Vinkius and switch between OpenAI, Anthropic, or Gemini without changing your integration code. full type safety, structured output guarantees, and dependency injection for testable agents.

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 Pydantic AI 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 Pydantic AI via MCP

Follow these steps to integrate the Quantive (Gtmhub) MCP Server with Pydantic AI.

Install Pydantic AI

Run pip install pydantic-ai

Replace the token

Replace [YOUR_TOKEN_HERE] with your Vinkius token

Run the agent

Save to agent.py and run: python agent.py

Explore tools

The agent discovers 10 tools from Quantive (Gtmhub) with type-safe schemas

Why Use Pydantic AI with the Quantive (Gtmhub) MCP Server

Pydantic AI provides unique advantages when paired with Quantive (Gtmhub) through the Model Context Protocol.

Full type safety: every MCP tool response is validated against Pydantic models, catching data inconsistencies before they reach your application

Model-agnostic architecture. switch between OpenAI, Anthropic, or Gemini without changing your Quantive (Gtmhub) integration code

Structured output guarantee: Pydantic AI ensures tool results conform to defined schemas, eliminating runtime type errors

Dependency injection system cleanly separates your Quantive (Gtmhub) connection logic from agent behavior for testable, maintainable code

Quantive (Gtmhub) + Pydantic AI Use Cases

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

Type-safe data pipelines: query Quantive (Gtmhub) with guaranteed response schemas, feeding validated data into downstream processing

API orchestration: chain multiple Quantive (Gtmhub) tool calls with Pydantic validation at each step to ensure data integrity end-to-end

Production monitoring: build validated alert agents that query Quantive (Gtmhub) and output structured, schema-compliant notifications

Testing and QA: use Pydantic AI's dependency injection to mock Quantive (Gtmhub) responses and write comprehensive agent tests

Quantive (Gtmhub) MCP Tools for Pydantic AI (10)

These 10 tools become available when you connect Quantive (Gtmhub) to Pydantic AI 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 Pydantic AI

Ready-to-use prompts you can give your Pydantic AI 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 Pydantic AI

Common issues when connecting Quantive (Gtmhub) to Pydantic AI through the Vinkius, and how to resolve them.

MCPServerHTTP not found

Update: pip install --upgrade pydantic-ai

Quantive (Gtmhub) + Pydantic AI FAQ

Common questions about integrating Quantive (Gtmhub) MCP Server with Pydantic AI.

How does Pydantic AI discover MCP tools?

Create an MCPServerHTTP instance with the server URL. Pydantic AI connects, discovers all tools, and generates typed Python interfaces automatically.

Does Pydantic AI validate MCP tool responses?

Yes. When you define result types as Pydantic models, every tool response is validated against the schema. Invalid data raises a clear error instead of silently corrupting your pipeline.

Can I switch LLM providers without changing MCP code?

Absolutely. Pydantic AI abstracts the model layer. your Quantive (Gtmhub) MCP integration works identically with OpenAI, Anthropic, Google, or any supported provider.