Quantive (Gtmhub) MCP Server for LlamaIndex 10 tools — connect in under 2 minutes
LlamaIndex specializes in data-aware AI agents that connect LLMs to structured and unstructured sources. Add Quantive (Gtmhub) as an MCP tool provider through the Vinkius and your agents can query, analyze, and act on live data alongside your existing indexes.
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Vinkius supports streamable HTTP and SSE.
import asyncio
from llama_index.tools.mcp import BasicMCPClient, McpToolSpec
from llama_index.core.agent.workflow import FunctionAgent
from llama_index.llms.openai import OpenAI
async def main():
# Your Vinkius token — get it at cloud.vinkius.com
mcp_client = BasicMCPClient("https://edge.vinkius.com/[YOUR_TOKEN_HERE]/mcp")
mcp_tool_spec = McpToolSpec(client=mcp_client)
tools = await mcp_tool_spec.to_tool_list_async()
agent = FunctionAgent(
tools=tools,
llm=OpenAI(model="gpt-4o"),
system_prompt=(
"You are an assistant with access to Quantive (Gtmhub). "
"You have 10 tools available."
),
)
response = await agent.run(
"What tools are available in Quantive (Gtmhub)?"
)
print(response)
asyncio.run(main())
* 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 Quantive (Gtmhub) MCP Server
Connect your Quantive (formerly Gtmhub) strategy platform to any AI agent and drive your organizational goals through natural conversation.
LlamaIndex agents combine Quantive (Gtmhub) tool responses with indexed documents for comprehensive, grounded answers. Connect 10 tools through the Vinkius and query live data alongside vector stores and SQL databases in a single turn — ideal for hybrid search, data enrichment, and analytical workflows.
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 LlamaIndex 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 LlamaIndex via MCP
Follow these steps to integrate the Quantive (Gtmhub) MCP Server with LlamaIndex.
Install dependencies
Run pip install llama-index-tools-mcp llama-index-llms-openai
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)
Why Use LlamaIndex with the Quantive (Gtmhub) MCP Server
LlamaIndex provides unique advantages when paired with Quantive (Gtmhub) through the Model Context Protocol.
Data-first architecture: LlamaIndex agents combine Quantive (Gtmhub) tool responses with indexed documents for comprehensive, grounded answers
Query pipeline framework lets you chain Quantive (Gtmhub) tool calls with transformations, filters, and re-rankers in a typed pipeline
Multi-source reasoning: agents can query Quantive (Gtmhub), a vector store, and a SQL database in a single turn and synthesize results
Observability integrations show exactly what Quantive (Gtmhub) tools were called, what data was returned, and how it influenced the final answer
Quantive (Gtmhub) + LlamaIndex Use Cases
Practical scenarios where LlamaIndex combined with the Quantive (Gtmhub) MCP Server delivers measurable value.
Hybrid search: combine Quantive (Gtmhub) real-time data with embedded document indexes for answers that are both current and comprehensive
Data enrichment: query Quantive (Gtmhub) to augment indexed data with live information before generating user-facing responses
Knowledge base agents: build agents that maintain and update knowledge bases by periodically querying Quantive (Gtmhub) for fresh data
Analytical workflows: chain Quantive (Gtmhub) queries with LlamaIndex's data connectors to build multi-source analytical reports
Quantive (Gtmhub) MCP Tools for LlamaIndex (10)
These 10 tools become available when you connect Quantive (Gtmhub) to LlamaIndex 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 LlamaIndex
Ready-to-use prompts you can give your LlamaIndex 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 LlamaIndex
Common issues when connecting Quantive (Gtmhub) to LlamaIndex through the Vinkius, and how to resolve them.
BasicMCPClient not found
pip install llama-index-tools-mcpQuantive (Gtmhub) + LlamaIndex FAQ
Common questions about integrating Quantive (Gtmhub) MCP Server with LlamaIndex.
How does LlamaIndex connect to MCP servers?
Can I combine MCP tools with vector stores?
Does LlamaIndex support async MCP calls?
Connect Quantive (Gtmhub) with your favorite client
Step-by-step setup guides for every MCP-compatible client and framework:
Anthropic's native desktop app for Claude with built-in MCP support.
AI-first code editor with integrated LLM-powered coding assistance.
GitHub Copilot in VS Code with Agent mode and MCP support.
Purpose-built IDE for agentic AI coding workflows.
Autonomous AI coding agent that runs inside VS Code.
Anthropic's agentic CLI for terminal-first development.
Python SDK for building production-grade OpenAI agent workflows.
Google's framework for building production AI agents.
Type-safe agent development for Python with first-class MCP support.
TypeScript toolkit for building AI-powered web applications.
TypeScript-native agent framework for modern web stacks.
Python framework for orchestrating collaborative AI agent crews.
Leading Python framework for composable LLM applications.
Data-aware AI agent framework for structured and unstructured sources.
Microsoft's framework for multi-agent collaborative conversations.
Connect Quantive (Gtmhub) to LlamaIndex
Get your token, paste the configuration, and start using 10 tools in under 2 minutes. No API key management needed.