How to Use the Agile CRM MCP in LlamaIndex

Index your CRM records into LlamaIndex for grounded, RAG-based sales insights.

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Connect Agile CRM MCP to LlamaIndex

Create your Vinkius account to connect Agile CRM 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 Agile CRM with LlamaIndex

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Convert CRM data to searchable LlamaIndex nodes

Turn your sales records into a queryable knowledge base. The output from `list_deals` and `list_tasks` feeds directly into your vector store for semantic retrieval. Your RAG application now understands your sales pipeline. It stops guessing and starts answering questions based on the current state of your CRM records.

Ground your AI responses with LlamaIndex

Eliminate hallucinations by forcing your agent to query the CRM index before replying to questions. Your agent fetches live data via `search_contact_by_email` to verify identity. This creates a unified source of truth. Your documents and CRM data sit side-by-side in your index, providing context for every generated response.

Filtering CRM data for specific LlamaIndex tasks

Use the allowed_tools filter to restrict which CRM operations your agent can perform. You control access to `list_contacts` and other sensitive operations within your indexer. This keeps your agent focused. Only the necessary tools are available during the indexing phase, preventing unauthorized data exposure.

Setup guide

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

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Common questions about Agile CRM MCP in LlamaIndex

Use the BasicMCPClient to connect to the server and pass the tools to your FunctionAgent. The system handles the conversion of CRM data into indexable format automatically.

Once you pull the data, you can store it in a vector index. This allows your agent to perform fuzzy matches and find related deals based on natural language queries.

Your contact names and email addresses are processed in memory and indexed into your local vector store. The server does not retain or log your personal customer data.

You can merge the tool outputs with your document loaders in a single pipeline. This builds a unified index that covers both your static internal files and dynamic CRM entries.

The connection lasts for the duration of your session. You decide when to refresh the index, allowing you to pull the latest sales figures whenever your application needs an update.

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