AskNicely MCP Server for LangChain 5 tools — connect in under 2 minutes
LangChain is the leading Python framework for composable LLM applications. Connect AskNicely through the 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 MCP SERVER
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({
"asknicely": {
"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 AskNicely, 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 AskNicely MCP Server
The AskNicely MCP Server provides a powerful natural language interface to your customer experience platform. Empower your AI agent to monitor your Net Promoter Score (NPS), retrieve real-time user feedback, and manage your contact survey workflows seamlessly.
LangChain's ecosystem of 500+ components combines seamlessly with AskNicely through native MCP adapters. Connect 5 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.
Key Capabilities
- NPS Monitoring — Retrieve your aggregate NPS statistics and track changes in customer sentiment over time.
- Response Analysis — Access detailed survey responses, including scores and customer comments, to identify pain points and success stories.
- Contact Management — List and audit your contact database, including when users were last surveyed.
- Survey Automation — Trigger new surveys for specific customers directly from your chat interface to capture immediate feedback.
- Real-time Statistics — Get instant summaries of your customer experience metrics without manual dashboard exports.
- Secure API Access — Uses your AskNicely API Key for safe and authenticated communication.
The AskNicely MCP Server exposes 5 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 AskNicely to LangChain via MCP
Follow these steps to integrate the AskNicely 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 5 tools from AskNicely via MCP
Why Use LangChain with the AskNicely MCP Server
LangChain provides unique advantages when paired with AskNicely through the Model Context Protocol.
The largest ecosystem of integrations, chains, and agents — combine AskNicely 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 AskNicely queries for multi-turn workflows
AskNicely + LangChain Use Cases
Practical scenarios where LangChain combined with the AskNicely MCP Server delivers measurable value.
RAG with live data: combine AskNicely tool results with vector store retrievals for answers grounded in both real-time and historical data
Autonomous research agents: LangChain agents query AskNicely, synthesize findings, and generate comprehensive research reports
Multi-tool orchestration: chain AskNicely tools with web scrapers, databases, and calculators in a single agent run
Production monitoring: use LangSmith to trace every AskNicely tool call, measure latency, and optimize your agent's performance
AskNicely MCP Tools for LangChain (5)
These 5 tools become available when you connect AskNicely to LangChain via MCP:
get_account_check
Verify AskNicely account connection
get_statistics
Get aggregate NPS statistics and summary
list_contacts
List contacts in your AskNicely account
list_responses
List NPS survey responses from AskNicely
trigger_survey
Add a contact and trigger a survey immediately
Example Prompts for AskNicely in LangChain
Ready-to-use prompts you can give your LangChain agent to start working with AskNicely immediately.
"Show me our current NPS statistics."
"List the last 5 survey responses with comments."
"Trigger an NPS survey for 'Jane Smith' at 'jane@example.com'."
Troubleshooting AskNicely MCP Server with LangChain
Common issues when connecting AskNicely to LangChain through the Vinkius, and how to resolve them.
MultiServerMCPClient not found
pip install langchain-mcp-adaptersAskNicely + LangChain FAQ
Common questions about integrating AskNicely 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 AskNicely with your favorite client
Step-by-step setup guides for every MCP-compatible client and framework:
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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 AskNicely to LangChain
Get your token, paste the configuration, and start using 5 tools in under 2 minutes. No API key management needed.