How to Use the Zenvia Conversion MCP in LlamaIndex

Q: How can I query past data with Zenvia Conversion and LlamaIndex?

You index the output of tools like listprospects into a vector store. Then, you perform semantic search on that index to answer questions about historical contacts without having to write complex filter logic.

Q: Does LlamaIndex help with Zenvia Conversion messaging?

Yes. By indexing sendtemplate usage and message contents, you can query the knowledge base to understand what kind of messages worked best for specific types of prospects in the past.

Q: Is creating leads reliable when using Zenvia Conversion with LlamaIndex?

The createlead tool provides structured output that can be indexed. This means you don't just record the creation event; you build a knowledge chunk about it, improving future retrieval accuracy.

Ground Zenvia Conversion queries with LlamaIndex knowledge indexing.

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Connect Zenvia Conversion MCP to LlamaIndex

Create your Vinkius account to connect Zenvia Conversion 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 Zenvia Conversion with LlamaIndex

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Search Past Interactions

Use `list_interactions` to get a history of communications for any prospect. LlamaIndex takes these raw logs and indexes them into your vector store. Later, you can ask natural language questions about those past sessions—like 'What did we discuss last month?'—and the system answers using actual data from Zenvia Conversion.

Build a Prospect Knowledge Base

Instead of just calling `get_prospect` for isolated facts, you index all prospect records. This means your knowledge base isn't just structured; it understands context. Developers combine this indexed API data with internal documents, allowing your agent to answer questions grounded in both Zenvia Conversion and your company policy.

Contextual Messaging Recommendations

The server offers tools like `send_template` and `list_labels`. By indexing the labels and historical message data, LlamaIndex helps recommend the best template or label to use before sending a message. This moves beyond simple API calls; it provides intelligent suggestions based on patterns found across all your stored Zenvia Conversion records.

Setup guide

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

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Independent Platform Disclaimer: Vinkius is an independent platform and is not affiliated with, endorsed by, sponsored by, verified by, or otherwise authorized by Zenvia Conversion. All third-party trademarks, logos, and brand names are the property of their respective owners. Their use on this website is strictly for informational purposes to identify service compatibility and interoperability.

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Common questions about Zenvia Conversion MCP in LlamaIndex

You index the output of tools like `list_prospects` into a vector store. Then, you perform semantic search on that index to answer questions about historical contacts without having to write complex filter logic.

Yes. By indexing `send_template` usage and message contents, you can query the knowledge base to understand what kind of messages worked best for specific types of prospects in the past.

You absolutely can. The system allows developers to merge external PDF reports or internal guides into the same index as your live API data, making everything queryable together.

The `create_lead` tool provides structured output that can be indexed. This means you don't just record the creation event; you build a knowledge chunk about it, improving future retrieval accuracy.

The server touches contact details and message content. When indexing this sensitive data, ensure your RAG application has strict access controls defined to prevent accidental exposure.

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