How to Use the Agency Elephant MCP in LlamaIndex

Q: How can I use LlamaIndex to create a searchable index of all my Agency Elephant leads?

You'd use the McpToolSpec to load the tools. Then you can either manually call listleads and ingest the results, or let a query engine agent call the tool automatically to answer questions and build its index over time.

Q: What's the process for making Agency Elephant tools available to a LlamaIndex FunctionAgent?

First, use pip install llama-index-tools-mcp. Then, you create the BasicMCPClient and pass it to McpToolSpec. Calling mcptoolspec.totoollistasync() gives you the tools to pass to your agent.

Turn your Agency Elephant data into a searchable knowledge base for your LlamaIndex RAG applications.

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Connect Agency Elephant MCP to LlamaIndex

Create your Vinkius account to connect Agency Elephant 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 Agency Elephant with LlamaIndex

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Index Your Entire Lead Funnel

This MCP server lets you pull your agency's operational data directly into a knowledge base. Use the `list_leads` and `list_campaigns` tools to get a complete snapshot of your current sales pipeline and marketing efforts. With LlamaIndex, this data is automatically indexed into a vector store. Now you can ask plain-English questions about your agency's performance. You get answers grounded in the actual data from Agency Elephant, not just your agent's best guess.

Query Your Agency's Performance

Once your data is indexed, you can have a conversation with it. Ask your LlamaIndex agent things like, 'Show me details for the lead Jane Smith' or 'Which leads are in the hot-prospects group?' Your agent uses the indexed results from past calls to `get_lead_details` and `list_lead_groups` to find the answer. This is the core of a RAG application: combining your question with a searchable knowledge base built from your live Agency Elephant data.

Ground Your LlamaIndex Agent in Live Data

Your RAG application doesn't have to rely on a stale index. The Agency Elephant tools keep your knowledge base fresh. Your agent can call `list_leads` on the fly to get information about prospects that were just created. LlamaIndex can be set up to call this MCP Server whenever its existing knowledge isn't enough to answer a question. This means your agent can accurately talk about brand-new leads or recently launched campaigns because it fetches the data directly from the source when it needs to.

Setup guide

Set up Agency Elephant 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 Agency Elephant 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 Agency Elephant tools.",
)
response = await agent.run("List recent Agency Elephant 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 Agency Elephant. 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 Agency Elephant MCP in LlamaIndex

You'd use the `McpToolSpec` to load the tools. Then you can either manually call `list_leads` and ingest the results, or let a query engine agent call the tool automatically to answer questions and build its index over time.

First, use `pip install llama-index-tools-mcp`. Then, you create the `BasicMCPClient` and pass it to `McpToolSpec`. Calling `mcp_tool_spec.to_tool_list_async()` gives you the tools to pass to your agent.

Yes. LlamaIndex is designed for this. You can create one index from your documents and another from the results of the `list_campaigns` tool. Your query engine can then search across both sources to give a complete answer.

LlamaIndex itself doesn't cache the API calls, but it ingests the *results* of those calls into its index. The freshness of your index depends on how often you run the tools like `list_leads` to update it.

Your agent will be working with sensitive lead information, campaign names, and user lists from Agency Elephant. The Vinkius platform processes these requests in an ephemeral, zero-trust environment. Your data isn't stored or logged by the platform.

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