Porsline MCP Server for LlamaIndexGive LlamaIndex instant access to 12 tools to Check Porsline Status, Create Folder, Create Survey, and more
LlamaIndex specializes in data-aware AI agents that connect LLMs to structured and unstructured sources. Add Porsline 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 App Connector for LlamaIndex
The Porsline app connector for LlamaIndex is a standout in the Customer Support category — giving your AI agent 12 tools to work with, ready to go from day one.
Vinkius delivers Streamable HTTP and SSE to any MCP client
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 Porsline. "
"You have 12 tools available."
),
)
response = await agent.run(
"What tools are available in Porsline?"
)
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 Porsline MCP Server
Connect your Porsline account to any AI agent and simplify your survey creation, response tracking, and feedback orchestration through natural conversation.
LlamaIndex agents combine Porsline 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.
What you can do
- Survey Management — List all surveys, retrieve detailed metadata, status, and summary for each form
- Response Tracking — Access individual responses and answers to stay on top of your user feedback
- Live Reporting — Query aggregate metrics and summary reports for any survey to understand performance
- Folder Coordination — List organizational folders to manage your survey distribution and project structure
- Direct Insights — Monitor your survey pipeline and engagement metrics directly from your agent
The Porsline 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.
All 12 Porsline tools available for LlamaIndex
When LlamaIndex connects to Porsline through Vinkius, your AI agent gets direct access to every tool listed below — spanning feedback-collection, form-builder, data-reporting, 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.
Verify connectivity
Create a folder
Create a survey
Duplicate a survey
Export responses
Get survey report
Get response details
Get survey details
List folders
List questions
List responses
List surveys
Connect Porsline to LlamaIndex via MCP
Follow these steps to wire Porsline into LlamaIndex. The entire setup takes under two minutes — your credentials stay safe behind the Vinkius.
Install dependencies
pip install llama-index-tools-mcp llama-index-llms-openaiReplace the token
[YOUR_TOKEN_HERE] with your Vinkius tokenRun the agent
agent.py and run: python agent.pyExplore tools
Why Use LlamaIndex with the Porsline MCP Server
LlamaIndex provides unique advantages when paired with Porsline through the Model Context Protocol.
Data-first architecture: LlamaIndex agents combine Porsline tool responses with indexed documents for comprehensive, grounded answers
Query pipeline framework lets you chain Porsline tool calls with transformations, filters, and re-rankers in a typed pipeline
Multi-source reasoning: agents can query Porsline, a vector store, and a SQL database in a single turn and synthesize results
Observability integrations show exactly what Porsline tools were called, what data was returned, and how it influenced the final answer
Porsline + LlamaIndex Use Cases
Practical scenarios where LlamaIndex combined with the Porsline MCP Server delivers measurable value.
Hybrid search: combine Porsline real-time data with embedded document indexes for answers that are both current and comprehensive
Data enrichment: query Porsline 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 Porsline for fresh data
Analytical workflows: chain Porsline queries with LlamaIndex's data connectors to build multi-source analytical reports
Example Prompts for Porsline in LlamaIndex
Ready-to-use prompts you can give your LlamaIndex agent to start working with Porsline immediately.
"List all my surveys in Porsline."
"Show me all active surveys and their response rates for the current quarter."
"Get the detailed results and analytics for the Customer Satisfaction Q2 survey."
Troubleshooting Porsline MCP Server with LlamaIndex
Common issues when connecting Porsline to LlamaIndex through the Vinkius, and how to resolve them.
BasicMCPClient not found
pip install llama-index-tools-mcpPorsline + LlamaIndex FAQ
Common questions about integrating Porsline MCP Server with LlamaIndex.