Fidelizador MCP Server for LlamaIndexGive LlamaIndex instant access to 8 tools to Create Contact, Create Mailing List, Delete Contact, and more
LlamaIndex specializes in data-aware AI agents that connect LLMs to structured and unstructured sources. Add Fidelizador as an MCP tool provider through Vinkius and your agents can query, analyze, and act on live data alongside your existing indexes.
Ask AI about this App Connector for LlamaIndex
The Fidelizador app connector for LlamaIndex 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 llama_index.tools.mcp import BasicMCPClient, McpToolSpec
from llama_index.core.agent.workflow import FunctionAgent
from llama_index.llms.openai import OpenAI
async def main():
# Your Vinkius token. get it at cloud.vinkius.com
mcp_client = BasicMCPClient("https://edge.vinkius.com/[YOUR_TOKEN_HERE]/mcp")
mcp_tool_spec = McpToolSpec(client=mcp_client)
tools = await mcp_tool_spec.to_tool_list_async()
agent = FunctionAgent(
tools=tools,
llm=OpenAI(model="gpt-4o"),
system_prompt=(
"You are an assistant with access to Fidelizador. "
"You have 8 tools available."
),
)
response = await agent.run(
"What tools are available in Fidelizador?"
)
print(response)
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.
LlamaIndex agents combine Fidelizador tool responses with indexed documents for comprehensive, grounded answers. Connect 8 tools through Vinkius and query live data alongside vector stores and SQL databases in a single turn. ideal for hybrid search, data enrichment, and analytical workflows.
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 LlamaIndex 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 LlamaIndex
When LlamaIndex 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 LlamaIndex via MCP
Follow these steps to wire Fidelizador into LlamaIndex. The entire setup takes under two minutes — your credentials stay safe behind the Vinkius.
Install dependencies
pip install llama-index-tools-mcp llama-index-llms-openaiReplace the token
[YOUR_TOKEN_HERE] with your Vinkius tokenRun the agent
agent.py and run: python agent.pyExplore tools
Why Use LlamaIndex with the Fidelizador MCP Server
LlamaIndex provides unique advantages when paired with Fidelizador through the Model Context Protocol.
Data-first architecture: LlamaIndex agents combine Fidelizador tool responses with indexed documents for comprehensive, grounded answers
Query pipeline framework lets you chain Fidelizador tool calls with transformations, filters, and re-rankers in a typed pipeline
Multi-source reasoning: agents can query Fidelizador, a vector store, and a SQL database in a single turn and synthesize results
Observability integrations show exactly what Fidelizador tools were called, what data was returned, and how it influenced the final answer
Fidelizador + LlamaIndex Use Cases
Practical scenarios where LlamaIndex combined with the Fidelizador MCP Server delivers measurable value.
Hybrid search: combine Fidelizador real-time data with embedded document indexes for answers that are both current and comprehensive
Data enrichment: query Fidelizador to augment indexed data with live information before generating user-facing responses
Knowledge base agents: build agents that maintain and update knowledge bases by periodically querying Fidelizador for fresh data
Analytical workflows: chain Fidelizador queries with LlamaIndex's data connectors to build multi-source analytical reports
Example Prompts for Fidelizador in LlamaIndex
Ready-to-use prompts you can give your LlamaIndex 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 LlamaIndex
Common issues when connecting Fidelizador to LlamaIndex through the Vinkius, and how to resolve them.
BasicMCPClient not found
pip install llama-index-tools-mcpFidelizador + LlamaIndex FAQ
Common questions about integrating Fidelizador MCP Server with LlamaIndex.