AskNicely MCP Server for LlamaIndex 5 tools — connect in under 2 minutes
LlamaIndex specializes in data-aware AI agents that connect LLMs to structured and unstructured sources. Add AskNicely 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 MCP SERVER
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 AskNicely. "
"You have 5 tools available."
),
)
response = await agent.run(
"What tools are available in AskNicely?"
)
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 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.
LlamaIndex agents combine AskNicely tool responses with indexed documents for comprehensive, grounded answers. Connect 5 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.
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 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 AskNicely to LlamaIndex via MCP
Follow these steps to integrate the AskNicely 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 5 tools from AskNicely
Why Use LlamaIndex with the AskNicely MCP Server
LlamaIndex provides unique advantages when paired with AskNicely through the Model Context Protocol.
Data-first architecture: LlamaIndex agents combine AskNicely tool responses with indexed documents for comprehensive, grounded answers
Query pipeline framework lets you chain AskNicely tool calls with transformations, filters, and re-rankers in a typed pipeline
Multi-source reasoning: agents can query AskNicely, a vector store, and a SQL database in a single turn and synthesize results
Observability integrations show exactly what AskNicely tools were called, what data was returned, and how it influenced the final answer
AskNicely + LlamaIndex Use Cases
Practical scenarios where LlamaIndex combined with the AskNicely MCP Server delivers measurable value.
Hybrid search: combine AskNicely real-time data with embedded document indexes for answers that are both current and comprehensive
Data enrichment: query AskNicely 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 AskNicely for fresh data
Analytical workflows: chain AskNicely queries with LlamaIndex's data connectors to build multi-source analytical reports
AskNicely MCP Tools for LlamaIndex (5)
These 5 tools become available when you connect AskNicely to LlamaIndex 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 LlamaIndex
Ready-to-use prompts you can give your LlamaIndex 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 LlamaIndex
Common issues when connecting AskNicely to LlamaIndex through the Vinkius, and how to resolve them.
BasicMCPClient not found
pip install llama-index-tools-mcpAskNicely + LlamaIndex FAQ
Common questions about integrating AskNicely 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 AskNicely 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 AskNicely to LlamaIndex
Get your token, paste the configuration, and start using 5 tools in under 2 minutes. No API key management needed.