How to Use the Beehiiv MCP in LlamaIndex

Q: How do I filter Beehiiv tools in LlamaIndex?

Use the allowedtools filter during initialization. This restricts the agent to only the specific functions you want it to perform.

Index your Beehiiv newsletter history into LlamaIndex for semantic search and grounded knowledge retrieval.

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Connect Beehiiv MCP to LlamaIndex

Create your Vinkius account to connect Beehiiv 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 Beehiiv with LlamaIndex

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Index newsletter content for LlamaIndex

Convert your archive into a searchable vector store. Use `list_posts` to pull content and feed it into the index for retrieval later. Your agent searches through past posts to answer questions about your publication history. It gets the actual text back from `get_post` to ground its answers.

Query Beehiiv stats via LlamaIndex

Combine raw performance data with your documents to identify trends. The agent calls `get_post_stats` and adds the results to the knowledge base. You avoid hallucinations by forcing the agent to query the live API. It pulls the latest numbers before generating a summary.

Manage Beehiiv metadata

Store publication settings in your index to keep the agent aware of current configurations. Use `get_publication` to ensure your agent knows the current naming or status of your newsletter. This keeps your RAG application current with the real state of your account. The agent sees exactly what you see in the dashboard.

Setup guide

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

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Common questions about Beehiiv MCP in LlamaIndex

It fetches data from the server and indexes it into your vector store. This allows your agent to perform semantic searches across your newsletters.

You can list them, but we recommend focusing on content. The server exposes subscriber data, which you can index if needed for your specific use case.

You need the MCP-specific tool spec library. Once configured, you treat the server as a standard tool source for your agent.

Use the allowed_tools filter during initialization. This restricts the agent to only the specific functions you want it to perform.

The server processes subscriber details in an ephemeral sandbox. Your data never touches persistent storage outside of your local index.

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