How to Use the Insightly MCP in LlamaIndex

Q: How do I configure the Insightly MCP Server with LlamaIndex?

Install the tool package via pip install llama-index-tools-mcp first. Initialize the BasicMCPClient with your Vinkius URL, wrap it in McpToolSpec, and pass the Insightly tools to your LlamaIndex agent.

Q: Can LlamaIndex filter which Insightly tools are exposed?

Yes, you can use the allowedtools parameter during LlamaIndex setup to restrict access. If you only want your LlamaIndex agent to see contacts, you can expose listcontacts and block the rest of the Insightly tools.

Q: Does this integration support semantic search over Insightly projects?

Yes, by passing the output of listprojects to LlamaIndex, the framework indexes project names and descriptions. This allows you to perform semantic search across all active and past Insightly projects.

Index live Insightly CRM data directly into LlamaIndex vector stores to eliminate agent hallucinations.

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Connect Insightly MCP to LlamaIndex

Create your Vinkius account to connect Insightly 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 Insightly with LlamaIndex

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Ground LlamaIndex queries in Insightly CRM data

The `list_opportunities` tool pulls active sales pipelines directly from Insightly into your LlamaIndex RAG application. Your LlamaIndex agent indexes this live Insightly data, allowing users to query current deal sizes without reading raw spreadsheets. This process stops your LlamaIndex LLM from hallucinating Insightly numbers. By feeding `list_projects` output straight into a LlamaIndex vector index, your queries about Insightly project delivery dates are backed by actual CRM records.

Build searchable knowledge bases from MCP Server tools

This MCP Server lets your LlamaIndex agent turn Insightly CRM objects into searchable documents using `list_contacts` and `get_contact`. LlamaIndex takes the structured JSON from these tools and creates semantic embeddings of your Insightly contacts on the fly. You can ask your LlamaIndex agent who the primary stakeholder is for a specific Insightly account. Your LlamaIndex agent queries `list_organisations`, indexes the result, and finds the exact Insightly company record instantly.

Query operational context in real time

The `list_tasks` tool provides a live feed of outstanding Insightly work items directly to your LlamaIndex indexing pipeline. LlamaIndex combines this with calendar schedules from `list_events` to build a unified timeline of Insightly team activities. Your LlamaIndex agent uses this combined data to answer complex Insightly scheduling questions. Your LlamaIndex agent checks `list_teams` and `list_users` to see who is assigned to overdue Insightly tasks without manual filtering.

Setup guide

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

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Common questions about Insightly MCP in LlamaIndex

Install the tool package via `pip install llama-index-tools-mcp` first. Initialize the `BasicMCPClient` with your Vinkius URL, wrap it in `McpToolSpec`, and pass the Insightly tools to your LlamaIndex agent.

Yes, you can use the `allowed_tools` parameter during LlamaIndex setup to restrict access. If you only want your LlamaIndex agent to see contacts, you can expose `list_contacts` and block the rest of the Insightly tools.

You can configure your LlamaIndex to refresh periodically by calling `list_leads` or `list_opportunities` before running a query. This ensures your LlamaIndex vector store reflects the latest Insightly CRM updates.

Yes, by passing the output of `list_projects` to LlamaIndex, the framework indexes project names and descriptions. This allows you to perform semantic search across all active and past Insightly projects.

Your lead profiles and contact records are processed in a zero-trust, ephemeral V8 sandbox. Vinkius secures your Insightly API tokens, and the raw CRM data is only used to populate your local LlamaIndex vector store via this secure MCP Server.

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