GetFeedback MCP Server for LlamaIndex 12 tools — connect in under 2 minutes
LlamaIndex specializes in data-aware AI agents that connect LLMs to structured and unstructured sources. Add GetFeedback 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 GetFeedback. "
"You have 12 tools available."
),
)
response = await agent.run(
"What tools are available in GetFeedback?"
)
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 GetFeedback MCP Server
Connect your GetFeedback account to any AI agent to automate your customer feedback and survey reporting workflows through the Model Context Protocol (MCP). GetFeedback is a powerful, mobile-friendly survey platform that helps brands collect and analyze customer sentiment in real-time. This MCP server enables you to retrieve survey results, monitor completion statuses, and trigger survey invitations directly through natural conversation.
LlamaIndex agents combine GetFeedback tool responses with indexed documents for comprehensive, grounded answers. Connect 12 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 Features
- Survey Orchestration — List all active surveys in your account and fetch detailed structural metadata for each form.
- Real-time Response Tracking — Retrieve customer feedback as it arrives, including detailed answer payloads and completion timestamps.
- Advanced Filtering — List survey responses filtered by status (started, completed) or created after a specific date for targeted reporting.
- Automated Invitations — Trigger survey emails to a list of recipients programmatically from your chat interface.
- Identity Oversight — Access global profile information for the authenticated GetFeedback user to ensure correct account context.
- Data Connectivity — Verify your API connection and account health to maintain seamless feedback loops.
- Asynchronous Monitoring — Fetch high-level response counts and status metrics to track survey performance instantly.
The GetFeedback MCP Server exposes 12 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 GetFeedback to LlamaIndex via MCP
Follow these steps to integrate the GetFeedback 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 12 tools from GetFeedback
Why Use LlamaIndex with the GetFeedback MCP Server
LlamaIndex provides unique advantages when paired with GetFeedback through the Model Context Protocol.
Data-first architecture: LlamaIndex agents combine GetFeedback tool responses with indexed documents for comprehensive, grounded answers
Query pipeline framework lets you chain GetFeedback tool calls with transformations, filters, and re-rankers in a typed pipeline
Multi-source reasoning: agents can query GetFeedback, a vector store, and a SQL database in a single turn and synthesize results
Observability integrations show exactly what GetFeedback tools were called, what data was returned, and how it influenced the final answer
GetFeedback + LlamaIndex Use Cases
Practical scenarios where LlamaIndex combined with the GetFeedback MCP Server delivers measurable value.
Hybrid search: combine GetFeedback real-time data with embedded document indexes for answers that are both current and comprehensive
Data enrichment: query GetFeedback 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 GetFeedback for fresh data
Analytical workflows: chain GetFeedback queries with LlamaIndex's data connectors to build multi-source analytical reports
GetFeedback MCP Tools for LlamaIndex (12)
These 12 tools become available when you connect GetFeedback to LlamaIndex via MCP:
check_api_limits
Verify connectivity
get_my_identity
Get user identity
get_response_details
Get response metadata
get_survey_details
Get survey metadata
get_survey_stats
Get response count
list_completed_feedback
Filter for completed
list_feedback_page
Paginated responses
list_recent_feedback
Filter by date
list_survey_responses
List feedback data
list_surveys
List all surveys
send_survey_invites
Trigger survey email
verify_api_connection
Check connection
Example Prompts for GetFeedback in LlamaIndex
Ready-to-use prompts you can give your LlamaIndex agent to start working with GetFeedback immediately.
"List all active surveys in my GetFeedback account."
"Show me the last 5 completed responses for survey '12345'."
"Send the 'Onboarding Survey' (ID: 98765) to ['user1@test.com', 'user2@test.com']."
Troubleshooting GetFeedback MCP Server with LlamaIndex
Common issues when connecting GetFeedback to LlamaIndex through the Vinkius, and how to resolve them.
BasicMCPClient not found
pip install llama-index-tools-mcpGetFeedback + LlamaIndex FAQ
Common questions about integrating GetFeedback 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 GetFeedback 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.
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Anthropic's agentic CLI for terminal-first development.
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Google's framework for building production AI agents.
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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 GetFeedback to LlamaIndex
Get your token, paste the configuration, and start using 12 tools in under 2 minutes. No API key management needed.