How to Use the Makeswift MCP in LlamaIndex

Q: How do I index Makeswift snapshots in LlamaIndex?

You configure the client to fetch snapshot data via getsnapshot and pass that output into your document loaders for vectorization.

Index your entire site structure into LlamaIndex for semantic queries and grounded content analysis.

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Connect Makeswift MCP to LlamaIndex

Create your Vinkius account to connect Makeswift 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 Makeswift with LlamaIndex

Ask AI about this MCP

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Claude

Perplexity

Semantic search across Makeswift pages

Feed the results of `list_pages` and `get_page` into your vector store. You turn static site routes into a searchable knowledge base for your AI. When you ask about site structure, the agent queries the index rather than hallucinating paths. It understands your site hierarchy because it has ingested the actual metadata.

Index snapshots for historical context

Use `list_snapshots` to pull historical content states into your RAG pipeline. The agent can compare current page data against past snapshots to identify changes. This creates a grounded history of your site. You aren't just looking at the live state; you're querying the evolution of your content over time.

Query site configuration in LlamaIndex

Map your hostnames and locales using `list_hostnames` and `list_locales` to build a unified site index. Your agent uses this to answer questions about global site settings. It removes the guesswork from multi-locale management. The agent knows exactly which hostnames are active because it queried them directly from the source.

Setup guide

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

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Common questions about Makeswift MCP in LlamaIndex

Yes. By using the MCP spec, LlamaIndex pulls site details and page lists directly into its index for semantic retrieval.

You configure the client to fetch snapshot data via `get_snapshot` and pass that output into your document loaders for vectorization.

It will. By indexing your page list and site details, the agent provides accurate, grounded answers about your current site setup.

Your site data stays within your local index. We use ephemeral sessions to ensure that no page content persists beyond the necessary processing window.

Access is restricted by your unique endpoint token. Your content is never exposed to external models unless you explicitly include it in your local vector store.

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