ZenHub MCP Server for LlamaIndex 8 tools — connect in under 2 minutes
LlamaIndex specializes in data-aware AI agents that connect LLMs to structured and unstructured sources. Add ZenHub as an MCP tool provider through 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 ZenHub. "
"You have 8 tools available."
),
)
response = await agent.run(
"What tools are available in ZenHub?"
)
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 ZenHub MCP Server
Connect your ZenHub account to any AI agent to streamline your agile project management on GitHub. This MCP server enables your agent to interact with pipelines, issues, estimates, and epics directly from natural language.
LlamaIndex agents combine ZenHub tool responses with indexed documents for comprehensive, grounded answers. Connect 8 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
- Board Visibility — List all pipelines and issues for specific GitHub repositories or ZenHub workspaces
- Agile Status Management — Move issues between pipelines to update their workflow status instantly
- Precision Estimating — Set and retrieve story point estimates for any GitHub issue
- Epic Oversight — List and inspect ZenHub epics and their constituent issues
- Release Tracking — Access release reports and progress metadata for your projects
The ZenHub MCP Server exposes 8 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 ZenHub to LlamaIndex via MCP
Follow these steps to integrate the ZenHub 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 8 tools from ZenHub
Why Use LlamaIndex with the ZenHub MCP Server
LlamaIndex provides unique advantages when paired with ZenHub through the Model Context Protocol.
Data-first architecture: LlamaIndex agents combine ZenHub tool responses with indexed documents for comprehensive, grounded answers
Query pipeline framework lets you chain ZenHub tool calls with transformations, filters, and re-rankers in a typed pipeline
Multi-source reasoning: agents can query ZenHub, a vector store, and a SQL database in a single turn and synthesize results
Observability integrations show exactly what ZenHub tools were called, what data was returned, and how it influenced the final answer
ZenHub + LlamaIndex Use Cases
Practical scenarios where LlamaIndex combined with the ZenHub MCP Server delivers measurable value.
Hybrid search: combine ZenHub real-time data with embedded document indexes for answers that are both current and comprehensive
Data enrichment: query ZenHub 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 ZenHub for fresh data
Analytical workflows: chain ZenHub queries with LlamaIndex's data connectors to build multi-source analytical reports
ZenHub MCP Tools for LlamaIndex (8)
These 8 tools become available when you connect ZenHub to LlamaIndex via MCP:
get_epic_data
Get details for a specific epic
get_repo_board
Get the ZenHub board for a repository
get_workspace_board
Get the ZenHub board for a specific workspace and repository
get_zenhub_issue_data
Get ZenHub-specific metadata for a GitHub issue
list_release_reports
List release reports for a repository
list_repo_epics
List all ZenHub epics for a repository
move_issue_between_pipelines
Move an issue to a different pipeline
set_issue_estimate
Set the story point estimate for an issue
Example Prompts for ZenHub in LlamaIndex
Ready-to-use prompts you can give your LlamaIndex agent to start working with ZenHub immediately.
"Show me the ZenHub board for repository ID '12345678'."
"Move issue #45 in repo '12345678' to the 'In Progress' pipeline (ID: '56789') in workspace '98765'."
"What are the estimates for all issues in the current epic?"
Troubleshooting ZenHub MCP Server with LlamaIndex
Common issues when connecting ZenHub to LlamaIndex through the Vinkius, and how to resolve them.
BasicMCPClient not found
pip install llama-index-tools-mcpZenHub + LlamaIndex FAQ
Common questions about integrating ZenHub 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 ZenHub 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 ZenHub to LlamaIndex
Get your token, paste the configuration, and start using 8 tools in under 2 minutes. No API key management needed.