How to Use the Medallia MCP in LlamaIndex

Q: Can I filter which Medallia tools LlamaIndex sees?

Absolutely, use the allowedtools filter during setup. This limits the agent to only the functions you want it to access.

Index live customer feedback into LlamaIndex for grounded, searchable insights from your Medallia data.

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Connect Medallia MCP to LlamaIndex

Create your Vinkius account to connect Medallia to LlamaIndex and route execution through our secure gateway. The platform manages server hosting, runtime updates, and security layers. Configuration requires no manual server provisioning.

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Setup Medallia with LlamaIndex

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Index survey responses for RAG

Use `list_responses` to pull raw customer feedback and dump it into your vector store. LlamaIndex then turns that data into a searchable knowledge base. Stop guessing what customers think. Query your index directly to get answers based on actual survey entries rather than static reports.

Ground answers in Medallia program details

Fetch current program configurations with `get_program_details` and keep your index updated. Your agent uses this live info to provide context-aware responses. When your data changes, your index reflects it immediately. You are querying the source of truth, not a stale cache.

Search feedback by term efficiently

Run `search_responses` to find specific customer complaints or praises. The MCP Server hands this data off to your indexer for instant semantic retrieval. It makes your feedback history accessible in seconds. You stop digging through dashboards and start finding patterns in the text.

Setup guide

Set up Medallia MCP in LlamaIndex

Prerequisites

Python 3.10+ installed
llama-index-tools-mcp package
Active Vinkius subscription with a valid endpoint token

1

Install dependencies
Run pip install llama-index-tools-mcp llama-index-llms-openai. The MCP tools package provides BasicMCPClient and McpToolSpec.
2

Connect with BasicMCPClient
Point BasicMCPClient to your Vinkius endpoint URL. Replace [YOUR_TOKEN_HERE] with your token from cloud.vinkius.com. Supports SSE and Streamable HTTP transports.
3

Convert to LlamaIndex tools
Call mcp_tool_spec.to_tool_list_async() to convert all Medallia MCP tools into native FunctionTool objects that any LlamaIndex agent can use.
4

Run with any LLM
Create a FunctionAgent with the tools and your preferred LLM. Swap OpenAI for Anthropic, Gemini, or any LlamaIndex-supported provider.

agent.py

from llama_index.tools.mcp import BasicMCPClient, McpToolSpec
from llama_index.core.agent.workflow import FunctionAgent
from llama_index.llms.openai import OpenAI

# Connect to the MCP
mcp_client = BasicMCPClient(
    "https://edge.vinkius.com/[YOUR_TOKEN_HERE]/mcp"
)
mcp_tool_spec = McpToolSpec(client=mcp_client)

# Convert MCP tools to LlamaIndex tools
tools = await mcp_tool_spec.to_tool_list_async()

# Create and run the agent
agent = FunctionAgent(
    tools=tools,
    llm=OpenAI(model="gpt-4o"),
    system_prompt="You have access to Medallia tools.",
)
response = await agent.run("List recent Medallia data")

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Common questions about Medallia MCP in LlamaIndex

Use the tool spec to fetch the data and pass it to your vector store pipeline. The integration handles the conversion from API output to indexable documents.

No, because the agent relies on the retrieved tool data. If the information isn't in your indexed Medallia responses, the agent will report that it cannot find the answer.

Absolutely, use the allowed_tools filter during setup. This limits the agent to only the functions you want it to access.

All survey data is processed in memory and never written to persistent logs. The connection is encrypted end-to-end and requires a unique endpoint token.

It does. You can trigger an index refresh whenever the server detects a new alert via the `list_alerts` tool.

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