How to Use the Postscript MCP in LlamaIndex

Index your SMS marketing history into a searchable knowledge base using LlamaIndex and the Postscript MCP Server.

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Connect Postscript MCP to LlamaIndex

Create your Vinkius account to connect Postscript 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 Postscript with LlamaIndex

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Query your SMS knowledge in LlamaIndex

Transform your SMS campaign data into a vector index. By calling `list_campaigns` and `get_campaign`, you feed historical performance data into your RAG pipeline. Your queries become grounded in reality. Instead of guessing performance, your agent searches the actual data returned by the server to answer questions about past revenue.

Automate subscriber indexing

Keep your knowledge base fresh by indexing subscriber updates. When you run `get_subscriber`, the output becomes a node in your graph for semantic retrieval. Context is everything. Your agent retrieves specific customer histories across thousands of records, allowing for hyper-personalized messaging strategies.

Bridge live data and static documents

Combine your internal marketing PDFs with live API stats. The server provides the tools to pull current account standing via `get_account_info` while the framework handles the document search. Decisions are backed by evidence. Your agent cross-references your current SMS strategy documents against live automation triggers found in `list_automations`.

Setup guide

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

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Common questions about Postscript MCP in LlamaIndex

It translates API responses into searchable nodes. You call `list_keywords` to populate your index with current SMS trends for better retrieval.

Yes, once you index the tool outputs, you can query your subscriber list naturally. The agent retrieves the relevant profiles and uses them to construct its response.

Your vector store resides where you deploy it. The server only fetches the subscriber or campaign data you explicitly choose to index.

You manage the crawl frequency in your agent settings. If you pull too much data, the server returns the appropriate status code for you to handle.

Only if you configure your vector store that way. You decide which data points from the server warrant long-term storage in your searchable knowledge base.

Use it with your favorite AI tools

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

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