How to Use the MasterGo MCP in LlamaIndex

Q: Can LlamaIndex search through MasterGo comments?

Yes. Use getcomments to pull discussion threads. Once indexed, your agent can search for specific feedback across multiple files.

Q: How do I restrict access to MasterGo files in LlamaIndex?

Use the allowedtools filter in your client setup. You can limit which projects the agent can see before it builds the index.

Index your design system into LlamaIndex. Turn MasterGo files into a searchable knowledge base for your AI.

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Connect MasterGo MCP to LlamaIndex

Create your Vinkius account to connect MasterGo 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 MasterGo with LlamaIndex

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Create a searchable index of MasterGo files

Convert your design file metadata into vector embeddings. The server pulls details via `get_file` and `list_files`, then LlamaIndex stores them for semantic retrieval. You can ask questions about your design system and get grounded answers. It stops the agent from guessing file locations.

Query design styles with LlamaIndex

Extract your color and typography tokens using `list_styles`. LlamaIndex indexes these definitions so you can search for specific design system assets. This creates a live reference for your agent. Whenever a question about design consistency comes up, it checks the source of truth.

Retrieve project history for RAG applications

Fetch historical file versions with `get_file_versions`. You can build a knowledge base that tracks how your designs evolved over time. This gives your agent context on past iterations. It makes it easier to answer why a specific design choice was made last month.

Setup guide

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

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Common questions about MasterGo MCP in LlamaIndex

You re-run your indexing pipeline to refresh the data. The server provides the latest state, and LlamaIndex updates your vector store.

Yes. Use `get_comments` to pull discussion threads. Once indexed, your agent can search for specific feedback across multiple files.

Use the allowed_tools filter in your client setup. You can limit which projects the agent can see before it builds the index.

No. You trigger the sync via your script. This prevents unnecessary API calls and keeps your index cost-effective.

Your design metadata is processed locally. We never store your raw files, and your endpoint token is used only for specific, requested operations.

Use it with your favorite AI tools

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

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