How to Use the FirstQuadrant MCP in LlamaIndex

Q: How do I index FirstQuadrant contact details in LlamaIndex?

Use the MCP tool spec to pull data from getcontactdetails or listcontacts. Convert these tool outputs into document nodes and load them straight into your vector index for retrieval.

Q: Can LlamaIndex trigger campaigns using FirstQuadrant?

Yes. You can register addtocampaign as a function tool within your LlamaIndex agent, allowing it to enroll prospects based on semantic search results.

Q: How does FirstQuadrant prevent hallucinations in LlamaIndex?

By providing raw, structured data through listpropertiesschema. Your agent relies on this schema instead of guessing what custom fields exist in your sales pipeline.

Q: Is my prospect search history kept private?

Yes. All data retrieved from listcontacts and getcontactdetails stays inside your local index environment, passing through an ephemeral sandbox that never caches your lead profiles.

Index live sales data and run targeted outreach sequences directly from your LlamaIndex knowledge base.

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Connect FirstQuadrant MCP to LlamaIndex

Create your Vinkius account to connect FirstQuadrant 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 FirstQuadrant with LlamaIndex

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Turn sales activities into queryable knowledge

The `list_activities` tool turns raw sales data into queryable knowledge by pulling live interaction logs. This MCP Server allows LlamaIndex to pull live data and index the results directly into a vector store for semantic search. Your agent can query past interactions to find out which messaging worked best. By analyzing actual sales histories instead of guessing, your agent writes highly relevant follow-up emails.

Ground outreach decisions in real CRM data

The `get_contact_details` tool grounds your outreach decisions in real CRM data. Your agent uses this tool to pull current prospect info before drafting any email or making a sequence decision. This direct connection means your RAG pipeline always has the ground truth. It checks the fields returned by `list_properties_schema` to ensure every personalized variable is accurate.

Automate LlamaIndex sequence enrollment

The `add_to_campaign` tool automates LlamaIndex sequence enrollment by triggering actual outbound actions. When your query engine identifies a hot prospect, it can immediately run `create_contact` and execute the campaign sequence. You don't need to write manual export scripts. The agent searches your indexed documents, matches them with active prospects in `list_contacts`, and pushes them into sequences.

Setup guide

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

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Common questions about FirstQuadrant MCP in LlamaIndex

Use the MCP tool spec to pull data from `get_contact_details` or `list_contacts`. Convert these tool outputs into document nodes and load them straight into your vector index for retrieval.

Yes. You can register `add_to_campaign` as a function tool within your LlamaIndex agent, allowing it to enroll prospects based on semantic search results.

By providing raw, structured data through `list_properties_schema`. Your agent relies on this schema instead of guessing what custom fields exist in your sales pipeline.

Yes, you can use the allowed tools filter in the tool spec to restrict your agent to read-only tools like `list_campaigns` or write tools like `create_contact`.

Yes. All data retrieved from `list_contacts` and `get_contact_details` stays inside your local index environment, passing through an ephemeral sandbox that never caches your lead profiles.

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