How do I find my Alchemer API Token and Secret?

Log in to Alchemer as an admin, navigate to **Account** > **Integrations** > **API Access**. You can generate and copy your `api_token` and `api_token_secret` there.

Which region should I choose?

Choose the region based on your account's data center: **US** (api.alchemer.com), **EU** (api.alchemer.eu), or **CA** (api.alchemer.ca).

Can I retrieve responses for a specific survey?

Yes! Use the `list_survey_responses` tool and provide the Survey ID. Your agent will retrieve the latest submissions, including timestamps and completion status.

Alchemer MCP Server for LlamaIndex 10 tools — connect in under 2 minutes

Name: Alchemer
Availability: InStock
Author: Vinkius

docs.llamaindex.ai/en/stable/module_guides/tools/mcp

Built by Vinkius GDPR 10 Tools Framework

LlamaIndex specializes in data-aware AI agents that connect LLMs to structured and unstructured sources. Add Alchemer as an MCP tool provider through Vinkius and your agents can query, analyze, and act on live data alongside your existing indexes.

Get MCP Server for AI Agents

ASK AI ABOUT THIS MCP SERVER

Open in ChatGPT Open in Claude Open in Perplexity

Vinkius supports streamable HTTP and SSE.

python

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 Alchemer. "
            "You have 10 tools available."
        ),
    )

    response = await agent.run(
        "What tools are available in Alchemer?"
    )
    print(response)

asyncio.run(main())

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Stream every event to Splunk, Datadog, or your own webhook in real-time

* 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 Alchemer MCP Server

Connect your Alchemer (formerly SurveyGizmo) account to your AI agent to unlock professional survey management and customer feedback orchestration. From auditing survey structures and questions to retrieving real-time responses and generating granular reports, your agent handles your feedback lifecycle through natural conversation.

LlamaIndex agents combine Alchemer tool responses with indexed documents for comprehensive, grounded answers. Connect 10 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 Orchestration — List and retrieve details for surveys, including their current status and technical metadata
Question Management — List and audit survey questions to ensure your data collection is precisely configured
Response Auditing — Retrieve and analyze individual or aggregated survey responses directly from chat
Reporting & Campaigns — List and manage survey reports and campaigns to monitor your data distribution and analysis
Contact Oversight — List and manage contact lists used for targeted survey distribution

The Alchemer MCP Server exposes 10 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 Alchemer to LlamaIndex via MCP

Follow these steps to integrate the Alchemer 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 10 tools from Alchemer

Why Use LlamaIndex with the Alchemer MCP Server

LlamaIndex provides unique advantages when paired with Alchemer through the Model Context Protocol.

Data-first architecture: LlamaIndex agents combine Alchemer tool responses with indexed documents for comprehensive, grounded answers

Query pipeline framework lets you chain Alchemer tool calls with transformations, filters, and re-rankers in a typed pipeline

Multi-source reasoning: agents can query Alchemer, a vector store, and a SQL database in a single turn and synthesize results

Observability integrations show exactly what Alchemer tools were called, what data was returned, and how it influenced the final answer

Alchemer + LlamaIndex Use Cases

Practical scenarios where LlamaIndex combined with the Alchemer MCP Server delivers measurable value.

Hybrid search: combine Alchemer real-time data with embedded document indexes for answers that are both current and comprehensive

Data enrichment: query Alchemer 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 Alchemer for fresh data

Analytical workflows: chain Alchemer queries with LlamaIndex's data connectors to build multi-source analytical reports

Alchemer MCP Tools for LlamaIndex (10)

These 10 tools become available when you connect Alchemer to LlamaIndex via MCP:

get_account_usage

Check account status

get_question_details

Get question metadata

get_response_details

Get response data

get_survey_details

Get survey metadata

list_contact_lists

List survey contacts

list_survey_campaigns

List distribution campaigns

list_survey_questions

List survey questions

list_survey_reports

List survey reports

list_survey_responses

List survey submissions

list_surveys

List account surveys

Example Prompts for Alchemer in LlamaIndex

Ready-to-use prompts you can give your LlamaIndex agent to start working with Alchemer immediately.

"List all active surveys in my Alchemer account."

"Show me the last 5 responses for survey ID 1234567."

"List all questions in the 'Customer Satisfaction' survey."

Troubleshooting Alchemer MCP Server with LlamaIndex

Common issues when connecting Alchemer to LlamaIndex through the Vinkius, and how to resolve them.

BasicMCPClient not found

Install: pip install llama-index-tools-mcp

Alchemer + LlamaIndex FAQ

Common questions about integrating Alchemer MCP Server with LlamaIndex.

How does LlamaIndex connect to MCP servers?

Use the MCP client adapter to create a connection. LlamaIndex discovers all tools and wraps them as query engine tools compatible with any LlamaIndex agent.

Can I combine MCP tools with vector stores?

Yes. LlamaIndex agents can query Alchemer tools and vector store indexes in the same turn, combining real-time and embedded data for grounded responses.