How to Use the Copilot CRM (Assembly) MCP in LlamaIndex

Query your Copilot CRM (Assembly) data using LlamaIndex to turn client records into a searchable knowledge base.

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Connect Copilot CRM (Assembly) MCP to LlamaIndex

Create your Vinkius account to connect Copilot CRM (Assembly) 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 Copilot CRM (Assembly) with LlamaIndex

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Index Copilot CRM (Assembly) data for search

Run `list_companies` and `list_clients` to feed your vector store. LlamaIndex turns these API responses into grounded knowledge your agent can query. Your RAG application now references live billing details from `list_invoices` instead of relying on stale documents. It keeps your answers current.

Ground AI answers in CRM facts

Use `list_custom_fields` to map your specific data types into the index. Your agent provides answers based on the actual schema used in your portal. Every tool call becomes a piece of searchable history. You stop the guesswork by forcing the agent to check the live source.

Automate client research with LlamaIndex

Fetch file metadata via `list_files` and add it to your retrieval pipeline. Your agent identifies relevant documents before answering a user question. It links your portal messages to your existing knowledge base. This creates a unified view of your entire client history.

Setup guide

Set up Copilot CRM (Assembly) 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 Copilot CRM (Assembly) 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 Copilot CRM (Assembly) tools.",
)
response = await agent.run("List recent Copilot CRM (Assembly) 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 Copilot CRM. 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 Copilot CRM (Assembly) MCP in LlamaIndex

The server fetches raw data which you then push into your vector store. Your agent queries this index to provide contextually accurate responses.

Absolutely, just wrap the tools using the provided spec class. This allows your agent to choose the right API call based on the user query.

It can. By indexing file metadata from the portal, your agent finds the right records quickly when a user asks about specific files.

That depends on your vector store capacity. The server provides the tools to extract the data, and you control the ingestion volume.

Access is restricted to the specific tools you authorize. All invoice data is processed through an ephemeral, isolated sandbox environment that clears after every query.

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