How to Use the Cordial MCP in LlamaIndex

Q: Can LlamaIndex update profiles in Cordial?

Yes, your agent can call upsertsubscriber when it needs to write new profile attributes back to your account. This action runs directly from your agent's function-calling toolset.

Q: How does Cordial search work inside LlamaIndex?

Your agent uses searchcampaignsbyname to find matching campaigns and then indexes the resulting JSON payload. This makes the campaign details searchable via semantic MCP queries.

Q: How do I list audience segments?

You call listaudiencesegments to retrieve segment IDs and subscriber counts. Your agent can use this information to filter index queries by specific user groups.

Index your Cordial subscriber data and campaign metrics directly into LlamaIndex vector stores for grounded RAG queries.

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Connect Cordial MCP to LlamaIndex

Create your Vinkius account to connect Cordial 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 Cordial with LlamaIndex

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Index contact attributes for LlamaIndex RAG

`list_contacts` pulls email addresses, opt-in statuses, and metadata directly into your LlamaIndex document store. This tool feeds raw subscriber profiles to your indexers to build a searchable database of user properties. Combining this with `get_subscriber_profile` lets you ground your agent's answers in actual, live customer data. Your queries pull from real-time profile states instead of stale database exports.

Search marketing campaigns with this MCP Server

`search_campaigns_by_name` locates active marketing runs and returns their schedules and high-level performance metrics. This MCP tool helps your indexer catalog past performance data for future analytical queries. Use `list_marketing_campaigns` alongside it to build a chronological index of your marketing history. Your LlamaIndex agent can then query this index to compare past campaign results.

Feed supplementary data tables to your index

`list_supplementary_data` retrieves metadata from your custom Cordial data collections. This tool exposes external tables used for message personalization so your agent can index custom relational structures. Your RAG pipeline queries these indexed tables to resolve complex context before generating email templates. It keeps your personalized content accurate without manual SQL exports.

Setup guide

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

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Common questions about Cordial MCP in LlamaIndex

Initialize the basic client and convert it to a tool spec using the LlamaIndex MCP adapter. Your pipeline can then fetch data from tools like `list_contacts` to build vector documents.

Yes, your agent can call `upsert_subscriber` when it needs to write new profile attributes back to your account. This action runs directly from your agent's function-calling toolset.

Your agent uses `search_campaigns_by_name` to find matching campaigns and then indexes the resulting JSON payload. This makes the campaign details searchable via semantic MCP queries.

You call `list_audience_segments` to retrieve segment IDs and subscriber counts. Your agent can use this information to filter index queries by specific user groups.

Your subscriber profiles and list memberships are processed in transit inside ephemeral V8 isolates. No custom attributes are ever written to persistent disk on our platform, keeping your customer records secure.

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