How do I get a Cordial API Key?

Log in to your Cordial account, go to **Settings > API Keys**, and create a new key. Ensure you have the necessary permissions to manage contacts and messages.

Can the agent trigger batch campaigns?

This integration currently focuses on listing and monitoring campaigns. To trigger sends, you should use the Cordial dashboard or specific automation workflows.

What profile attributes can the agent update?

The agent can update any custom attributes defined in your Cordial schema, as well as channel subscription preferences, as long as the API Key has write access.

Cordial MCP Server for LangChain 10 tools — connect in under 2 minutes

Name: Cordial
Availability: InStock
Author: Vinkius

python.langchain.com/docs/integrations/tools/mcp

Built by Vinkius GDPR 10 Tools Framework

LangChain is the leading Python framework for composable LLM applications. Connect Cordial through Vinkius and LangChain agents can call every tool natively. combine them with retrievers, memory, and output parsers for sophisticated AI pipelines.

Get MCP Server for AI Agents

ASK AI ABOUT THIS MCP SERVER

Open in ChatGPT Open in Claude Open in Perplexity

Vinkius supports streamable HTTP and SSE.

python

import asyncio
from langchain_mcp_adapters.client import MultiServerMCPClient
from langchain_openai import ChatOpenAI
from langgraph.prebuilt import create_react_agent

async def main():
    # Your Vinkius token. get it at cloud.vinkius.com
    async with MultiServerMCPClient({
        "cordial": {
            "transport": "streamable_http",
            "url": "https://edge.vinkius.com/[YOUR_TOKEN_HERE]/mcp",
        }
    }) as client:
        tools = client.get_tools()
        agent = create_react_agent(
            ChatOpenAI(model="gpt-4o"),
            tools,
        )
        response = await agent.ainvoke({
            "messages": [{
                "role": "user",
                "content": "Using Cordial, show me what tools are available.",
            }]
        })
        print(response["messages"][-1].content)

asyncio.run(main())

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DLPData protection

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Ed25519Audit chain

<40msKill switch

Stream every event to Splunk, Datadog, or your own webhook in real-time

* Every MCP server runs on Vinkius-managed infrastructure inside AWS - a purpose-built runtime with per-request V8 isolates, Ed25519 signed audit chains, and sub-40ms cold starts optimized for native MCP execution. See our infrastructure

About Cordial MCP Server

Integrate Cordial, the cross-channel marketing platform, directly into your AI workflow. Manage your audience segments, trigger automated messages, and monitor campaign performance using natural language.

LangChain's ecosystem of 500+ components combines seamlessly with Cordial through native MCP adapters. Connect 10 tools via Vinkius and use ReAct agents, Plan-and-Execute strategies, or custom agent architectures. with LangSmith tracing giving full visibility into every tool call, latency, and token cost.

What you can do

Audience Management — List and search for subscribers, and update profile attributes seamlessly.
Campaign Monitoring — Track the performance of batch and transactional email/SMS campaigns.
Automation Control — Monitor and manage active message automation workflows.
Data Insights — Access supplementary data collections and account metadata via chat.

The Cordial MCP Server exposes 10 tools through the Vinkius. Connect it to LangChain in under two minutes — no API keys to rotate, no infrastructure to provision, no vendor lock-in. Your configuration, your data, your control.

How to Connect Cordial to LangChain via MCP

Follow these steps to integrate the Cordial MCP Server with LangChain.

Install dependencies

Run pip install langchain langchain-mcp-adapters langgraph langchain-openai

Replace the token

Replace [YOUR_TOKEN_HERE] with your Vinkius token

Run the agent

Save the code and run python agent.py

Explore tools

The agent discovers 10 tools from Cordial via MCP

Why Use LangChain with the Cordial MCP Server

LangChain provides unique advantages when paired with Cordial through the Model Context Protocol.

The largest ecosystem of integrations, chains, and agents. combine Cordial MCP tools with 500+ LangChain components

Agent architecture supports ReAct, Plan-and-Execute, and custom strategies with full MCP tool access at every step

LangSmith tracing gives you complete visibility into tool calls, latencies, and token usage for production debugging

Memory and conversation persistence let agents maintain context across Cordial queries for multi-turn workflows

Cordial + LangChain Use Cases

Practical scenarios where LangChain combined with the Cordial MCP Server delivers measurable value.

RAG with live data: combine Cordial tool results with vector store retrievals for answers grounded in both real-time and historical data

Autonomous research agents: LangChain agents query Cordial, synthesize findings, and generate comprehensive research reports

Multi-tool orchestration: chain Cordial tools with web scrapers, databases, and calculators in a single agent run

Production monitoring: use LangSmith to trace every Cordial tool call, measure latency, and optimize your agent's performance

Cordial MCP Tools for LangChain (10)

These 10 tools become available when you connect Cordial to LangChain via MCP:

get_account_details

Resolves system-level account identifiers, plan configuration, and core platform settings. Get metadata about your Cordial account

get_subscriber_profile

Resolves granular profile data including custom attributes, device tokens, and list memberships. Get full profile and attributes for a subscriber

list_audience_segments

Resolves list identity properties such as segment IDs, names, and subscriber counts. List contact segments and audience groups

list_automation_messages

Resolves active automated message definitions and workflow status for triggered communications. List active automated message workflows

list_contacts

Resolves contact identity properties including email addresses, channel opt-ins, and attribute metadata across the Cordial system boundary. List subscribers in Cordial

list_marketing_campaigns

Resolves campaign identity and status, including scheduling data and high-level performance indicators. List marketing campaigns and their performance

list_messages

Resolves batch and transactional message definitions, including templates, subject lines, and sender profiles. List batch and transactional messages

list_supplementary_data

Resolves metadata for custom data collections used for message personalization. List supplement collections (external data tables)

search_campaigns_by_name

Resolves a subset of campaigns matching the name criteria across the platform boundary. Search for marketing campaigns by name

upsert_subscriber

Creates or updates a profile with identity properties, channel preferences, and custom attributes. Create or update a subscriber profile

Example Prompts for Cordial in LangChain

Ready-to-use prompts you can give your LangChain agent to start working with Cordial immediately.

"List the most recent marketing campaigns and their open rates."

"Show me the profile for the subscriber 'user@example.com'."

"Check the size of our 'Active Customers' list."

Troubleshooting Cordial MCP Server with LangChain

Common issues when connecting Cordial to LangChain through the Vinkius, and how to resolve them.

MultiServerMCPClient not found

Install: pip install langchain-mcp-adapters

Cordial + LangChain FAQ

Common questions about integrating Cordial MCP Server with LangChain.

How does LangChain connect to MCP servers?

Use langchain-mcp-adapters to create an MCP client. LangChain discovers all tools and wraps them as native LangChain tools compatible with any agent type.

Which LangChain agent types work with MCP?

All agent types including ReAct, OpenAI Functions, and custom agents work with MCP tools. The tools appear as standard LangChain tools after the adapter wraps them.