How to Use the Maropost MCP in LlamaIndex

Q: How does LlamaIndex work with this Maropost MCP Server?

You wrap the MCP client in the McpToolSpec. LlamaIndex can then treat every Maropost tool, like createcontact, as a function it can call to either execute an action or gather data for indexing.

Q: Can I control which Maropost tools my LlamaIndex agent can use?

Yes. The McpToolSpec has an allowedtools filter. You can give an agent access to only the read-only 'list' and 'get' tools, preventing it from accidentally creating contacts or modifying campaigns.

Q: How do I connect my Maropost data to a vector database with LlamaIndex?

LlamaIndex handles that. When your agent calls a tool like listcampaigns, the McpToolSpec returns the data. You then pass this data to a LlamaIndex VectorStoreIndex, which handles the embedding and storage in your chosen database.

Turn your Maropost API output into a searchable knowledge base with LlamaIndex. Ask questions, get answers from your own data.

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Connect Maropost MCP to LlamaIndex

Create your Vinkius account to connect Maropost 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 Maropost with LlamaIndex

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Index Your Marketing History

This server lets your LlamaIndex agent do more than just call an API; it lets you index the results. Your agent can run `list_campaigns` and `get_campaign_details` for everything you've ever sent. The output of these tools is then fed into a vector store, creating a searchable history of your marketing activities. Now you can ask your agent, 'Which campaigns last quarter mentioned our new feature?' It will query the indexed data to find the answer, grounded in actual results from the Maropost API. You're not just executing tasks; you're building institutional knowledge.

Build a RAG App on Your Contacts

Use this MCP Server to combine live Maropost data with your documents in a LlamaIndex RAG application. Your agent can use `list_lists` and `list_contacts_in_list` to fetch and index your audience segments. You can then ask complex questions that require joining this data with other sources. For example: 'Summarize the support tickets for contacts on the VIP list.' The agent uses the `list_contacts_in_list` tool to get the VIPs from Maropost, then looks up their tickets in your Zendesk index. It's a powerful way to get a complete customer view.

Query Your Automation Logic

Stop clicking through UI menus to understand your marketing automation. Have your LlamaIndex agent call `list_workflows` and `get_workflow_details` to index the structure of every workflow in your Maropost account. This creates a queryable map of your automation logic. Once indexed, you can ask plain-English questions like, 'Which workflows send an email after a cart is abandoned?' or 'Show me all workflows that use the `new_subscriber` tag.' Your agent finds the answer from its knowledge base, saving you hours of manual auditing.

Setup guide

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

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Independent Platform Disclaimer: Vinkius is an independent platform and is not affiliated with, endorsed by, sponsored by, verified by, or otherwise authorized by Maropost. All third-party trademarks, logos, and brand names are the property of their respective owners. Their use on this website is strictly for informational purposes to identify service compatibility and interoperability.

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Common questions about Maropost MCP in LlamaIndex

You wrap the MCP client in the `McpToolSpec`. LlamaIndex can then treat every Maropost tool, like `create_contact`, as a function it can call to either execute an action or gather data for indexing.

Yes. The `McpToolSpec` has an `allowed_tools` filter. You can give an agent access to only the read-only 'list' and 'get' tools, preventing it from accidentally creating contacts or modifying campaigns.

LlamaIndex is built for knowledge augmentation. The primary goal isn't just to call `get_report_details`; it's to index the output of that call so you can ask questions about your reports later. It's about turning API data into a persistent, searchable asset.

LlamaIndex handles that. When your agent calls a tool like `list_campaigns`, the `McpToolSpec` returns the data. You then pass this data to a LlamaIndex `VectorStoreIndex`, which handles the embedding and storage in your chosen database.

Your security model has two parts. The Vinkius MCP Server provides secure, ephemeral execution for the API call itself. But once your LlamaIndex agent indexes the output of `get_contact` or `list_contacts_in_list`, that contact data is now stored in your vector database. You are responsible for securing that database.

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