How to Use the Loomly MCP in LlamaIndex

Q: How does LlamaIndex manage Loomly media assets?

By indexing the output of listmediaassets, your agent gains a searchable directory of your library. It can then match existing media to your posts based on descriptive tags or filenames.

Index your social media history into LlamaIndex for semantic search and grounded answers about your brand performance.

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Connect Loomly MCP to LlamaIndex

Create your Vinkius account to connect Loomly 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 Loomly with LlamaIndex

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Index Loomly history in LlamaIndex

Turn your past posts into a searchable knowledge base. By using `list_calendar_posts` and indexing the results, your RAG application can answer questions about what you posted last month and why. This transforms raw API data into a source of truth. You stop guessing about past strategy and start querying your own history to inform future content decisions.

Search social interactions with LlamaIndex

Use `list_post_interactions` to feed comment data into your vector store. Your agent can then identify common audience pain points or positive feedback trends across all your managed calendars. This provides a foundation for data-backed content creation. Instead of relying on intuition, your agent queries the indexed interactions to verify what topics actually resonate with your audience.

Ground content drafting with LlamaIndex

Create new posts using `create_new_post` while referencing indexed brand guidelines. Your agent retrieves relevant context from your vector store before drafting, ensuring tone consistency. This reduces hallucinations by forcing the agent to rely on your actual historical data. It turns your past successes into a blueprint for every new post you generate.

Setup guide

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

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Common questions about Loomly MCP in LlamaIndex

Use the MCP tool spec to fetch your posts and comments, then push that data into your vector store. This allows your agent to perform semantic searches across your entire calendar history.

Your agent can periodically re-run the tool calls to update the index with fresh data. This ensures your RAG application always references the latest post interactions and calendar status.

Data is processed only within your secure runtime environment. The MCP server restricts access to your calendar metadata and post content, ensuring that your private brand information stays protected during the indexing process.

You can include your profile info in the index to give your agent context about your specific account permissions. This helps the agent understand which calendars it has authority to modify.

By indexing the output of `list_media_assets`, your agent gains a searchable directory of your library. It can then match existing media to your posts based on descriptive tags or filenames.

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