How to Use the Infinity MCP in LlamaIndex

Turn your Infinity project data into a searchable knowledge base for LlamaIndex using this MCP Server.

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Connect Infinity MCP to LlamaIndex

Create your Vinkius account to connect Infinity 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 Infinity with LlamaIndex

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Index project data with LlamaIndex

Use `list_board_items` to grab your project status and feed it into a vector store. LlamaIndex then treats your Infinity tasks as a searchable knowledge base. This allows you to ask questions about your project history that are grounded in real data. You get answers based on what is actually in your boards.

Query board attributes via MCP Server

Call `list_board_attributes` to understand the schema of your boards. LlamaIndex uses this metadata to build better indexes for your specific workflow. This makes your RAG application smarter about the relationships between your tasks. It ensures the search results stay relevant to your board structure.

Search historical comments in LlamaIndex

Pull discussions from your tasks using `list_item_comments` and add them to your index. This captures the context behind your project decisions. Your agent can now look back at past conversations to explain why a certain task was updated. It provides a deeper memory for your projects.

Setup guide

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

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Common questions about Infinity MCP in LlamaIndex

Yes. By pulling data through the server, you can index your board items into a searchable format. This grounds your agent's knowledge in your live project data.

You use the provided tool spec to fetch your board data. Once fetched, you pass it to your indexer to create a unified knowledge base.

It does. You just convert the tools to a list and pass them to the agent. It will then know how to query your boards when needed.

You can use the server's list tools to grab specific folders or boards. This lets you index only the data that matters to your current query.

The server uses your unique endpoint token to verify every request. Only the specific board data you target is accessed by the agent.

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