How to Use the Alchemer MCP in LlamaIndex

Index your Alchemer survey results into LlamaIndex to create a searchable knowledge base of customer sentiment.

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Connect Alchemer MCP to LlamaIndex

Create your Vinkius account to connect Alchemer 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 Alchemer with LlamaIndex

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Index Alchemer data for LlamaIndex RAG

Turn survey submissions into vector embeddings. You call `list_survey_responses` and feed the output into your index to make past feedback queryable. The system grounds agent answers in actual survey data. No more guessing about what customers said in previous campaigns.

Query Alchemer metadata with LlamaIndex

Use `get_survey_details` to pull context before asking questions. The agent retrieves the metadata and uses it to frame the search over your feedback index. You avoid hallucinations by forcing the agent to fetch current survey structures first. It knows exactly which fields exist before it tries to query them.

Filter Alchemer tools in LlamaIndex agents

Restrict the agent to only use specific tools for data retrieval. You set the `allowed_tools` list to keep the agent focused on survey operations. This keeps your application footprint small. You only expose the tools necessary for the specific RAG task at hand.

Setup guide

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

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Common questions about Alchemer MCP in LlamaIndex

The tool output is converted into a format the vector store understands. Once indexed, your agent searches through past responses to answer user queries.

You can pull data from every survey in your account. The agent aggregates the responses into your unified index for global searching.

Access is restricted to your authenticated session. Your survey submission data is never stored outside your infrastructure and remains private to your agents.

The MCP integration is fully asynchronous. You fetch tool lists and execute queries without blocking your application runtime.

It interacts with your survey metadata, contact lists, and individual submission records. Your agent only performs the actions you explicitly permit.

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