How to Use the Cometly MCP in LlamaIndex

Q: How do I index Cometly campaign performance into a LlamaIndex vector store?

You load the tools using McpToolSpec and call totoollistasync(). Your agent runs getcampaignstats to fetch the raw marketing data, which LlamaIndex then parses and inserts into your document index.

Q: Can I use LlamaIndex to query historical Cometly customer journeys?

Yes. By querying the indexed output of listcontacts, your agent can perform semantic searches over past customer touchpoints to find out which ad channels yield the highest conversion rates.

Q: How do I setup the Cometly MCP Server with LlamaIndex?

Install llama-index-tools-mcp and initialize the basic client with your Vinkius URL. Pass the tool list to your FunctionAgent to let your pipeline track conversions and inspect ad accounts.

Q: Does LlamaIndex support filtering which marketing tools the agent can access?

Yes. You can use the allowedtools filter in LlamaIndex to restrict your agent to read-only actions like listcampaigns or allow write operations like tracklead.

Index live Cometly marketing attribution and campaign performance data directly into your LlamaIndex vector stores.

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Connect Cometly MCP to LlamaIndex

Create your Vinkius account to connect Cometly 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 Cometly with LlamaIndex

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Index live Cometly stats for LlamaIndex RAG pipelines

Running `get_campaign_stats` and `list_campaigns` pulls raw performance metrics directly into your LlamaIndex vector store. This lets you ask natural language questions about your marketing ROI. Instead of reading raw JSON, you query your LlamaIndex index to find which ad angles drive the most valuable leads. The agent retrieves the grounded data from your past Cometly API calls, preventing hallucinated performance metrics.

Query customer journeys using LlamaIndex semantic search

By running `list_contacts` through this MCP Server, LlamaIndex indexes the touchpoints and conversion histories of your actual customers. This turns flat attribution logs into a searchable knowledge base. You can then query this index to find patterns in how leads convert. The LlamaIndex agent searches the vector store of contact profiles to explain the exact paths users took before your agent triggers `track_purchase` for new buyers.

Feed real-time events from indexed document sources

Executing `track_event` or `track_lead` lets your LlamaIndex agent instantly log conversions derived from processed documents. Connect your document parsers to your marketing tracking using this MCP Server. This bridges the gap between unstructured business documents and your marketing metrics. The LlamaIndex agent extracts the customer details from your indexed files and fires a precise `track_purchase` call to keep your attribution models accurate.

Setup guide

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

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Common questions about Cometly MCP in LlamaIndex

You load the tools using `McpToolSpec` and call `to_tool_list_async()`. Your agent runs `get_campaign_stats` to fetch the raw marketing data, which LlamaIndex then parses and inserts into your document index.

Yes. By querying the indexed output of `list_contacts`, your agent can perform semantic searches over past customer touchpoints to find out which ad channels yield the highest conversion rates.

Install `llama-index-tools-mcp` and initialize the basic client with your Vinkius URL. Pass the tool list to your `FunctionAgent` to let your pipeline track conversions and inspect ad accounts.

Yes. You can use the `allowed_tools` filter in LlamaIndex to restrict your agent to read-only actions like `list_campaigns` or allow write operations like `track_lead`.

Your marketing data is protected by the Vinkius zero-trust MCP architecture. When fetching data via `list_events` or `list_ad_accounts`, the connection uses a single token-based credential, and the server runs in an isolated environment that stores no customer logs.

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