How to Use the AlisQI MCP in LlamaIndex

Q: Can I filter AlisQI data before indexing in LlamaIndex?

Yes. The listresults tool accepts filters, so you only index the specific datasets you care about.

Q: How do I handle choice lists with LlamaIndex?

You use listchoicelists to pull the valid options for your fields. This helps your agent format queries that match your system requirements.

Index your quality data into LlamaIndex for semantic search and grounded answers.

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Connect AlisQI MCP to LlamaIndex

Create your Vinkius account to connect AlisQI 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 AlisQI with LlamaIndex

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Index AlisQI results in LlamaIndex

Use `list_results` to pull quality data and inject it directly into your LlamaIndex knowledge base. Your agent turns these records into searchable vector embeddings. This makes your historical QMS data queryable alongside your documents. You get answers based on actual test results instead of guessing.

Search attachment metadata in LlamaIndex

Retrieve document links with `get_result_attachments` and add them to your index. Your RAG app can then cite specific files when answering questions about product quality. This connects raw test results to the actual documentation stored in your system. Your agent finds the right file faster than a manual search.

Verify API status within LlamaIndex

Run `get_api_info` as a diagnostic tool before your agent attempts a retrieval. This prevents your indexer from failing during a connection hiccup. It ensures your knowledge base stays current by confirming the server is ready. You stop wasting tokens on failed requests.

Setup guide

Set up AlisQI 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 AlisQI 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 AlisQI tools.",
)
response = await agent.run("List recent AlisQI 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 AlisQI. 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 AlisQI MCP in LlamaIndex

The server provides a tool list that your agent uses to fetch results. You then pass these results into your indexer to build a searchable knowledge base.

No. By grounding your queries in tool-retrieved data, your agent cites the specific record ID rather than making up quality figures.

Yes. The `list_results` tool accepts filters, so you only index the specific datasets you care about.

You use `list_choice_lists` to pull the valid options for your fields. This helps your agent format queries that match your system requirements.

Data is encrypted during transit and processed in an ephemeral sandbox. Your QMS records are only indexed when you call the tools, keeping control strictly in your hands.

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