How to Use the Fairing MCP in LlamaIndex

Q: Does the Fairing MCP server support semantic indexing?

It provides the raw data through tools like listquestions. You then feed that output into your LlamaIndex pipeline to build your knowledge base.

Index your Fairing survey results into LlamaIndex for semantic search and grounded answers.

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Connect Fairing MCP to LlamaIndex

Create your Vinkius account to connect Fairing 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 Fairing with LlamaIndex

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Query historical Fairing data in LlamaIndex

Convert your survey responses into a searchable knowledge base. By calling `list_responses` and indexing the output, you give your agent a library of past customer sentiment to reference. Stop guessing what customers think and let your agent search the actual data. It retrieves specific feedback using `get_response` whenever a question requires a factual basis.

Ground AI answers with Fairing tool outputs

Prevent hallucinations by forcing your agent to use `get_insights` before answering questions about customer intent. The agent treats the API data as the primary source of truth. This ensures that every summary or insight provided is backed by live records. You simply define the query, and the server fetches the underlying survey details.

Index survey metadata with LlamaIndex

Use `list_surveys` and `get_survey_details` to build a semantic index of your active marketing campaigns. This allows the agent to understand the structure of your questions. Your agent can now compare response patterns across different surveys. It accesses the index to find relevant context before it makes a recommendation.

Setup guide

Set up Fairing 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 Fairing 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 Fairing tools.",
)
response = await agent.run("List recent Fairing data")

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Independent Platform Disclaimer: Vinkius is an independent platform and is not affiliated with, endorsed by, sponsored by, verified by, or otherwise authorized by Fairing. All third-party trademarks, logos, and brand names are the property of their respective owners. Their use on this website is strictly for informational purposes to identify service compatibility and interoperability.

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Common questions about Fairing MCP in LlamaIndex

You use the MCP client to expose the tools as a tool spec. Once configured, your LlamaIndex agent can call these tools to fetch data and add it to your vector store.

Absolutely. Once the data is indexed, your agent can perform semantic searches across your survey history. It uses the tools to pull the text, which is then turned into searchable vectors.

It provides the raw data through tools like `list_questions`. You then feed that output into your LlamaIndex pipeline to build your knowledge base.

All requests go through the Vinkius managed endpoint. You control which tools the agent can use, ensuring it only touches the survey data you authorize.

It retrieves survey questions, individual customer responses, and account-level insights. This data is handled in memory and indexed according to your LlamaIndex configuration.

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