How to Use the Range MCP in LlamaIndex

Q: Can LlamaIndex query historical Range updates for RAG?

Yes. The indexer uses the getupdate tool to fetch historical check-ins, allowing your RAG pipeline to search across your team's entire work history.

Q: How do I look up details for a specific team member?

Use the getuser tool with the target user's ID to fetch their active status, profile details, and current team alignment.

Index your Range team updates into LlamaIndex vector stores to search past blockers and goals without manual logging.

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Connect Range MCP to LlamaIndex

Create your Vinkius account to connect Range to LlamaIndex — we handle the hosting, security, and runtime updates so you don't have to. No server setup required.

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Setup Range with LlamaIndex

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Indexing team updates in LlamaIndex

This MCP Server lets your LlamaIndex pipelines pull check-ins via `list_updates` and turn them into searchable vector embeddings. When you run a LlamaIndex query about past technical debt, the agent retrieves actual context from `get_snippet` calls to ground the response.

Mapping daily tasks to core objectives

This MCP Server connects daily tasks to core objectives in LlamaIndex. The agent uses `list_objectives` to fetch organizational targets, indexing them alongside your daily check-in nodes using `get_objective` to structure the raw text.

Retrieving team structures instantly

This MCP Server retrieves team structures to build an active map inside LlamaIndex. The agent calls `list_teams` and `list_users` to build an active map of your organization inside your index, allowing new hires to query LlamaIndex to find out who is working on specific goals using `get_team`.

Setup guide

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

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Common questions about Range MCP in LlamaIndex

Install the MCP tool indexer and configure the basic client with your server URL. You can then load updates and snippets directly into your vector store index.

Yes. The indexer uses the `get_update` tool to fetch historical check-ins, allowing your RAG pipeline to search across your team's entire work history.

Yes, you can enable resource loading when configuring the client to let LlamaIndex read raw check-in text directly instead of just executing tools.

Use the `get_user` tool with the target user's ID to fetch their active status, profile details, and current team alignment.

All retrieved check-ins, objectives, and user profiles are processed in an isolated Vinkius endpoint acting as an ephemeral bridge that never caches your team's proprietary data.

Use it with your favorite AI tools

Connect this server to Cursor, Claude, VS Code, and more.

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