Fidelizador MCP Server for LangChainGive LangChain instant access to 8 tools to Create Contact, Create Mailing List, Delete Contact, and more
LangChain is the leading Python framework for composable LLM applications. Connect Fidelizador through Vinkius and LangChain agents can call every tool natively. combine them with retrievers, memory, and output parsers for sophisticated AI pipelines.
Ask AI about this App Connector for LangChain
The Fidelizador app connector for LangChain is a standout in the Marketing Automation category — giving your AI agent 8 tools to work with, ready to go from day one.
Vinkius delivers Streamable HTTP and SSE to any MCP client
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({
"fidelizador": {
"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 Fidelizador, show me what tools are available.",
}]
})
print(response["messages"][-1].content)
asyncio.run(main())
* 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 Fidelizador MCP Server
Connect your Fidelizador account to any AI agent and take full control of your email marketing and automation workflows through natural conversation.
LangChain's ecosystem of 500+ components combines seamlessly with Fidelizador through native MCP adapters. Connect 8 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 Orchestration — List and manage your email contacts programmatically, including creating, updating, and deleting profiles directly from your agent
- Campaign Management — Monitor your active and past email campaigns and retrieve detailed performance statistics and metadata programmatically
- List Intelligence — Create and manage mailing lists (segmentations) to maintain a structured and high-fidelity organization of your audience
- Relational Integrity — Access complete contact directories and retrieve granular details like phone numbers and custom data points
- System Monitoring — Check campaign statuses and manage subscriber lifecycles directly through your agent for instant marketing reporting
The Fidelizador MCP Server exposes 8 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.
All 8 Fidelizador tools available for LangChain
When LangChain connects to Fidelizador through Vinkius, your AI agent gets direct access to every tool listed below — spanning audience-segmentation, loyalty-programs, personalized-campaigns, and more. Every call is secured with network, filesystem, subprocess, and code evaluation entitlements inside a sandboxed runtime. Beyond a simple connection, you get a full AI Gateway with real-time visibility into agent activity, enterprise governance, and optimized token usage.
Create a new contact
Create a new mailing list
Delete a contact
Get campaign details
List email campaigns
List contacts in Fidelizador
List mailing lists
Update an existing contact
Connect Fidelizador to LangChain via MCP
Follow these steps to wire Fidelizador into LangChain. The entire setup takes under two minutes — your credentials stay safe behind the Vinkius.
Install dependencies
pip install langchain langchain-mcp-adapters langgraph langchain-openaiReplace the token
[YOUR_TOKEN_HERE] with your Vinkius tokenRun the agent
python agent.pyExplore tools
Why Use LangChain with the Fidelizador MCP Server
LangChain provides unique advantages when paired with Fidelizador through the Model Context Protocol.
The largest ecosystem of integrations, chains, and agents. combine Fidelizador 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 Fidelizador queries for multi-turn workflows
Fidelizador + LangChain Use Cases
Practical scenarios where LangChain combined with the Fidelizador MCP Server delivers measurable value.
RAG with live data: combine Fidelizador tool results with vector store retrievals for answers grounded in both real-time and historical data
Autonomous research agents: LangChain agents query Fidelizador, synthesize findings, and generate comprehensive research reports
Multi-tool orchestration: chain Fidelizador tools with web scrapers, databases, and calculators in a single agent run
Production monitoring: use LangSmith to trace every Fidelizador tool call, measure latency, and optimize your agent's performance
Example Prompts for Fidelizador in LangChain
Ready-to-use prompts you can give your LangChain agent to start working with Fidelizador immediately.
"List all my email campaigns in Fidelizador."
"Create a new contact 'John Doe' (john@example.com) in Fidelizador."
"Show me the details for campaign ID '101'."
Troubleshooting Fidelizador MCP Server with LangChain
Common issues when connecting Fidelizador to LangChain through the Vinkius, and how to resolve them.
MultiServerMCPClient not found
pip install langchain-mcp-adaptersFidelizador + LangChain FAQ
Common questions about integrating Fidelizador MCP Server with LangChain.
How does LangChain connect to MCP servers?
langchain-mcp-adapters to create an MCP client. LangChain discovers all tools and wraps them as native LangChain tools compatible with any agent type.