How to Use the Assembly MCP in LlamaIndex

Q: Can LlamaIndex search through Assembly workspaces?

Yes, the agent can call listworkspaces to discover environments and then use getworkspace to inspect details. This data can be indexed directly into a vector store for semantic querying.

Index live Assembly workspaces and client notes directly into your LlamaIndex knowledge graphs.

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Connect Assembly MCP to LlamaIndex

Create your Vinkius account to connect Assembly 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 Assembly with LlamaIndex

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Build RAG pipelines with live Assembly notes

LlamaIndex thrives on structured knowledge, and this MCP Server feeds it raw data. By calling `list_notes` and `get_note`, your agent pulls real-time updates directly into your vector index. You no longer have to export static CSVs or write custom sync cron jobs. The model reads these notes, indexes them, and makes them searchable. When a user asks about recent account updates, LlamaIndex queries the vector store, pulls the exact text from `get_note`, and answers with absolute precision.

Semantic search across workspaces and companies

Stop letting your workspace data sit in silos. This MCP Server lets your LlamaIndex pipelines query active structures via `list_workspaces` and `get_company`. Your agent can index these relationships to build a semantic map of your entire business. When a team member asks who is working on a specific account, the framework searches the index. It uses `get_user` and `list_clients` to find the exact matches, grounding its answers in your live Assembly directory.

Ground LlamaIndex queries in real company metadata

Hallucinations kill production agents. By giving your LlamaIndex setup direct access to `list_companies` and `get_client`, you ensure every response is backed by actual records. The agent verifies the metadata before answering. This setup uses the Vinkius hosting layer to keep queries fast. Your agent can run asynchronous queries to pull fresh company profiles, keeping your local vector index constantly updated without hitting rate limits.

Setup guide

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

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Common questions about Assembly MCP in LlamaIndex

You use the LlamaIndex MCP tool spec to connect to your Vinkius endpoint. Once connected, convert the server tools into a tool list and pass them to your FunctionAgent or indexer.

Yes, the agent can call `list_workspaces` to discover environments and then use `get_workspace` to inspect details. This data can be indexed directly into a vector store for semantic querying.

It does. You can load tools asynchronously using the standard LlamaIndex async methods. This prevents your RAG pipelines from stalling while pulling large client lists with `list_clients`.

Yes, you can use the allowed tools filter during initialization to restrict access. If you only want your agent to read notes, you can limit it to `list_notes` and `get_note`.

Your company records, workspace setups, notes, clients, and user details are never cached or stored. Vinkius runs this connector in a secure, single-tenant V8 sandbox. All data transmissions are encrypted end-to-end, keeping your team directory completely private.

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