Userback MCP Server for LangChainGive LangChain instant access to 6 tools to Create Feedback Entry, Get Feedback Details, Get Project Details, and more
LangChain is the leading Python framework for composable LLM applications. Connect Userback through Vinkius and LangChain agents can call every tool natively. combine them with retrievers, memory, and output parsers for sophisticated AI pipelines.
Ask AI about this App Connector for LangChain
The Userback app connector for LangChain is a standout in the Developer Tools category — giving your AI agent 6 tools to work with, ready to go from day one.
Vinkius delivers Streamable HTTP and SSE to any MCP client
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({
"userback": {
"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 Userback, 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 Userback MCP Server
Connect your Userback account to any AI agent and simplify how you collect and manage visual feedback, bug reports, and user suggestions through natural conversation.
LangChain's ecosystem of 500+ components combines seamlessly with Userback through native MCP adapters. Connect 6 tools via 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
- Feedback Management — List all feedback entries and retrieve detailed metadata, screenshots, and comments for specific reports.
- Project Control — List and query feedback projects to keep your development and design work organized.
- Direct Creation — Programmatically create new feedback entries or bug reports for specific projects via AI.
- Team Visibility — List account users and collaborators to understand your organization's review team.
- Status Tracking — Monitor the progress of feedback items and verify if issues have been resolved.
The Userback MCP Server exposes 6 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.
All 6 Userback tools available for LangChain
When LangChain connects to Userback through Vinkius, your AI agent gets direct access to every tool listed below — spanning visual-feedback, bug-reporting, user-experience, 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.
Create a new feedback entry
Get details for a specific feedback
Get details for a specific project
List account users
List Userback feedbacks
List Userback projects
Connect Userback to LangChain via MCP
Follow these steps to wire Userback into LangChain. The entire setup takes under two minutes — your credentials stay safe behind the Vinkius.
Install dependencies
pip install langchain langchain-mcp-adapters langgraph langchain-openaiReplace the token
[YOUR_TOKEN_HERE] with your Vinkius tokenRun the agent
python agent.pyExplore tools
Why Use LangChain with the Userback MCP Server
LangChain provides unique advantages when paired with Userback through the Model Context Protocol.
The largest ecosystem of integrations, chains, and agents. combine Userback 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 Userback queries for multi-turn workflows
Userback + LangChain Use Cases
Practical scenarios where LangChain combined with the Userback MCP Server delivers measurable value.
RAG with live data: combine Userback tool results with vector store retrievals for answers grounded in both real-time and historical data
Autonomous research agents: LangChain agents query Userback, synthesize findings, and generate comprehensive research reports
Multi-tool orchestration: chain Userback tools with web scrapers, databases, and calculators in a single agent run
Production monitoring: use LangSmith to trace every Userback tool call, measure latency, and optimize your agent's performance
Example Prompts for Userback in LangChain
Ready-to-use prompts you can give your LangChain agent to start working with Userback immediately.
"List all feedback projects in my Userback account."
"Show me the latest bug reports for the 'Product App v2' project."
"Create a new suggestion: 'Add dark mode support' to project '10293'."
Troubleshooting Userback MCP Server with LangChain
Common issues when connecting Userback to LangChain through the Vinkius, and how to resolve them.
MultiServerMCPClient not found
pip install langchain-mcp-adaptersUserback + LangChain FAQ
Common questions about integrating Userback 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.