TeamGantt MCP Server for LlamaIndexGive LlamaIndex instant access to 12 tools to Create New Task, Get Account Profile, Get Project Details, and more
LlamaIndex specializes in data-aware AI agents that connect LLMs to structured and unstructured sources. Add TeamGantt as an MCP tool provider through Vinkius and your agents can query, analyze, and act on live data alongside your existing indexes.
Ask AI about this App Connector for LlamaIndex
The TeamGantt app connector for LlamaIndex is a standout in the Productivity category — giving your AI agent 12 tools to work with, ready to go from day one.
Vinkius delivers Streamable HTTP and SSE to any MCP client
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 TeamGantt. "
"You have 12 tools available."
),
)
response = await agent.run(
"What tools are available in TeamGantt?"
)
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 TeamGantt MCP Server
Connect your TeamGantt account to any AI agent and simplify how you manage your project timelines, task assignments, and team resources through natural conversation.
LlamaIndex agents combine TeamGantt tool responses with indexed documents for comprehensive, grounded answers. Connect 12 tools through 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
- Project Oversight — List all projects in your account and retrieve detailed metadata and configuration for specific Gantt charts.
- Task Management — Create, update, and delete tasks with full control over start/end dates and completion percentages.
- Timeline Coordination — Create dependencies between tasks to ensure your project logic remains sound and automated.
- Resource Tracking — List available resources (people and equipment) to optimize team allocation across projects.
- Milestone Planning — List and query major project goals (milestones) and sub-task checklists.
- Account Visibility — Fetch your user profile and verify account configurations directly from the agent.
The TeamGantt MCP Server exposes 12 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.
All 12 TeamGantt tools available for LlamaIndex
When LlamaIndex connects to TeamGantt through Vinkius, your AI agent gets direct access to every tool listed below — spanning gantt-charts, project-planning, task-management, and more. Every call is secured with network, filesystem, subprocess, and code evaluation entitlements inside a sandboxed runtime. Beyond a simple connection, you get a full AI Gateway with real-time visibility into agent activity, enterprise governance, and optimized token usage.
Add task to project
Get user info
Get project info
List sub-tasks
Get task details
g. Task A must finish before Task B starts). Create Gantt link
List users and labels
List major goals
List tasks in project
List TeamGantt projects
Delete task
). Update task status/dates
Connect TeamGantt to LlamaIndex via MCP
Follow these steps to wire TeamGantt into LlamaIndex. The entire setup takes under two minutes — your credentials stay safe behind the Vinkius.
Install dependencies
pip install llama-index-tools-mcp llama-index-llms-openaiReplace the token
[YOUR_TOKEN_HERE] with your Vinkius tokenRun the agent
agent.py and run: python agent.pyExplore tools
Why Use LlamaIndex with the TeamGantt MCP Server
LlamaIndex provides unique advantages when paired with TeamGantt through the Model Context Protocol.
Data-first architecture: LlamaIndex agents combine TeamGantt tool responses with indexed documents for comprehensive, grounded answers
Query pipeline framework lets you chain TeamGantt tool calls with transformations, filters, and re-rankers in a typed pipeline
Multi-source reasoning: agents can query TeamGantt, a vector store, and a SQL database in a single turn and synthesize results
Observability integrations show exactly what TeamGantt tools were called, what data was returned, and how it influenced the final answer
TeamGantt + LlamaIndex Use Cases
Practical scenarios where LlamaIndex combined with the TeamGantt MCP Server delivers measurable value.
Hybrid search: combine TeamGantt real-time data with embedded document indexes for answers that are both current and comprehensive
Data enrichment: query TeamGantt 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 TeamGantt for fresh data
Analytical workflows: chain TeamGantt queries with LlamaIndex's data connectors to build multi-source analytical reports
Example Prompts for TeamGantt in LlamaIndex
Ready-to-use prompts you can give your LlamaIndex agent to start working with TeamGantt immediately.
"List all active projects in my TeamGantt account."
"Show me the tasks for 'Website Launch Q4' (ID: 10293)."
"Mark task '88231' as 100% complete."
Troubleshooting TeamGantt MCP Server with LlamaIndex
Common issues when connecting TeamGantt to LlamaIndex through the Vinkius, and how to resolve them.
BasicMCPClient not found
pip install llama-index-tools-mcpTeamGantt + LlamaIndex FAQ
Common questions about integrating TeamGantt MCP Server with LlamaIndex.