ZenHub MCP Server for LangChain 8 tools — connect in under 2 minutes
LangChain is the leading Python framework for composable LLM applications. Connect ZenHub through the Vinkius and LangChain agents can call every tool natively — combine them with retrievers, memory, and output parsers for sophisticated AI pipelines.
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Vinkius supports streamable HTTP and SSE.
import asyncio
from langchain_mcp_adapters.client import MultiServerMCPClient
from langchain_openai import ChatOpenAI
from langgraph.prebuilt import create_react_agent
async def main():
# Your Vinkius token — get it at cloud.vinkius.com
async with MultiServerMCPClient({
"zenhub": {
"transport": "streamable_http",
"url": "https://edge.vinkius.com/[YOUR_TOKEN_HERE]/mcp",
}
}) as client:
tools = client.get_tools()
agent = create_react_agent(
ChatOpenAI(model="gpt-4o"),
tools,
)
response = await agent.ainvoke({
"messages": [{
"role": "user",
"content": "Using ZenHub, show me what tools are available.",
}]
})
print(response["messages"][-1].content)
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.
LangChain's ecosystem of 500+ components combines seamlessly with ZenHub through native MCP adapters. Connect 8 tools via the Vinkius and use ReAct agents, Plan-and-Execute strategies, or custom agent architectures — with LangSmith tracing giving full visibility into every tool call, latency, and token cost.
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 LangChain 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 LangChain via MCP
Follow these steps to integrate the ZenHub MCP Server with LangChain.
Install dependencies
Run pip install langchain langchain-mcp-adapters langgraph langchain-openai
Replace the token
Replace [YOUR_TOKEN_HERE] with your Vinkius token
Run the agent
Save the code and run python agent.py
Explore tools
The agent discovers 8 tools from ZenHub via MCP
Why Use LangChain with the ZenHub MCP Server
LangChain provides unique advantages when paired with ZenHub through the Model Context Protocol.
The largest ecosystem of integrations, chains, and agents — combine ZenHub MCP tools with 500+ LangChain components
Agent architecture supports ReAct, Plan-and-Execute, and custom strategies with full MCP tool access at every step
LangSmith tracing gives you complete visibility into tool calls, latencies, and token usage for production debugging
Memory and conversation persistence let agents maintain context across ZenHub queries for multi-turn workflows
ZenHub + LangChain Use Cases
Practical scenarios where LangChain combined with the ZenHub MCP Server delivers measurable value.
RAG with live data: combine ZenHub tool results with vector store retrievals for answers grounded in both real-time and historical data
Autonomous research agents: LangChain agents query ZenHub, synthesize findings, and generate comprehensive research reports
Multi-tool orchestration: chain ZenHub tools with web scrapers, databases, and calculators in a single agent run
Production monitoring: use LangSmith to trace every ZenHub tool call, measure latency, and optimize your agent's performance
ZenHub MCP Tools for LangChain (8)
These 8 tools become available when you connect ZenHub to LangChain 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 LangChain
Ready-to-use prompts you can give your LangChain 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 LangChain
Common issues when connecting ZenHub to LangChain through the Vinkius, and how to resolve them.
MultiServerMCPClient not found
pip install langchain-mcp-adaptersZenHub + LangChain FAQ
Common questions about integrating ZenHub MCP Server with LangChain.
How does LangChain connect to MCP servers?
langchain-mcp-adapters to create an MCP client. LangChain discovers all tools and wraps them as native LangChain tools compatible with any agent type.Which LangChain agent types work with MCP?
Can I trace MCP tool calls in LangSmith?
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 LangChain
Get your token, paste the configuration, and start using 8 tools in under 2 minutes. No API key management needed.