Where do I find my Agendor API Token?

Log in to Agendor, navigate to **Menu** > **Integrações** (Integrations) > **Token da API**. You can generate and copy your unique token from there. It will be used in the `Authorization` header as `Token your-token`.

What is an 'Upsert' in Agendor?

An Upsert is a combination of Update and Insert. It allows you to send contact data, and Agendor will either create a new record or update an existing one if the email or CNPJ already exists, preventing duplicates.

Can I see my team's tasks via the agent?

Yes! Use the `list_tasks` tool to retrieve upcoming or pending tasks for any user or organization in your CRM.

Agendor MCP Server for LlamaIndex 6 tools — connect in under 2 minutes

Name: Agendor
Availability: InStock
Author: Vinkius

docs.llamaindex.ai/en/stable/module_guides/tools/mcp

Built by Vinkius GDPR 6 Tools Framework

LlamaIndex specializes in data-aware AI agents that connect LLMs to structured and unstructured sources. Add Agendor as an MCP tool provider through Vinkius and your agents can query, analyze, and act on live data alongside your existing indexes.

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 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 Agendor. "
            "You have 6 tools available."
        ),
    )

    response = await agent.run(
        "What tools are available in Agendor?"
    )
    print(response)

asyncio.run(main())

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* 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 Agendor MCP Server

Connect your Agendor account to your AI agent to unlock professional sales orchestration and CRM management. From creating new leads and organizations to auditing your sales pipeline and managing task workflows, your agent handles your sales ecosystem through natural conversation.

LlamaIndex agents combine Agendor tool responses with indexed documents for comprehensive, grounded answers. Connect 6 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

Lead & Contact Management — Create, update, and list people and organizations to keep your CRM data current
Pipeline Orchestration — List active deals, monitor sales funnels, and retrieve details for specific deal stages
Task Management — List and create tasks to ensure your team never misses a follow-up or critical deadline
Upsert Capability — Seamlessly create or update records based on email or CNPJ to prevent duplicate data entry
Sales Insights — Quickly identify high-value opportunities or overdue deals directly from your chat interface

The Agendor MCP Server exposes 6 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.

How to Connect Agendor to LlamaIndex via MCP

Follow these steps to integrate the Agendor MCP Server with LlamaIndex.

Install dependencies

Run pip install llama-index-tools-mcp llama-index-llms-openai

Replace the token

Replace [YOUR_TOKEN_HERE] with your Vinkius token

Run the agent

Save to agent.py and run: python agent.py

Explore tools

The agent discovers 6 tools from Agendor

Why Use LlamaIndex with the Agendor MCP Server

LlamaIndex provides unique advantages when paired with Agendor through the Model Context Protocol.

Data-first architecture: LlamaIndex agents combine Agendor tool responses with indexed documents for comprehensive, grounded answers

Query pipeline framework lets you chain Agendor tool calls with transformations, filters, and re-rankers in a typed pipeline

Multi-source reasoning: agents can query Agendor, a vector store, and a SQL database in a single turn and synthesize results

Observability integrations show exactly what Agendor tools were called, what data was returned, and how it influenced the final answer

Agendor + LlamaIndex Use Cases

Practical scenarios where LlamaIndex combined with the Agendor MCP Server delivers measurable value.

Hybrid search: combine Agendor real-time data with embedded document indexes for answers that are both current and comprehensive

Data enrichment: query Agendor 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 Agendor for fresh data

Analytical workflows: chain Agendor queries with LlamaIndex's data connectors to build multi-source analytical reports

Agendor MCP Tools for LlamaIndex (6)

These 6 tools become available when you connect Agendor to LlamaIndex via MCP:

create_organization

Instantiate a new organization record securely within Agendor

create_person

Can optionally link the person to a company. Instantiate a new person profile natively within the Agendor CRM

list_deals

Retrieve highly active sales opportunities and negotiation pipelines tracked in Agendor

list_organizations

Retrieve a directory of institutional organizations, companies, and business entities in the CRM

list_people

Retrieve a comprehensive directory of person profiles registered in your Agendor CRM

list_tasks

Retrieve the chronological queue of upcoming activities and follow-ups scheduled for the team

Example Prompts for Agendor in LlamaIndex

Ready-to-use prompts you can give your LlamaIndex agent to start working with Agendor immediately.

"List the last 5 people added to my Agendor CRM."

"Show me all deals in the 'Qualified' stage of my pipeline."

"Create a new organization named 'Tech Innovations' with domain 'techinn.com'."

Troubleshooting Agendor MCP Server with LlamaIndex

Common issues when connecting Agendor to LlamaIndex through the Vinkius, and how to resolve them.

BasicMCPClient not found

Install: pip install llama-index-tools-mcp

Agendor + LlamaIndex FAQ

Common questions about integrating Agendor MCP Server with LlamaIndex.

How does LlamaIndex connect to MCP servers?

Use the MCP client adapter to create a connection. LlamaIndex discovers all tools and wraps them as query engine tools compatible with any LlamaIndex agent.

Can I combine MCP tools with vector stores?

Yes. LlamaIndex agents can query Agendor tools and vector store indexes in the same turn, combining real-time and embedded data for grounded responses.