How to Use the Adversus MCP in LlamaIndex

Q: How do I load Adversus MCP tools into LlamaIndex?

Run pip install llama-index-tools-mcp first. Set up BasicMCPClient with your endpoint, wrap it in McpToolSpec, and call totoollistasync() to pass them to your FunctionAgent.

Q: Can LlamaIndex filter which Adversus tools the agent sees?

You can restrict access using the allowedtools parameter. This prevents the agent from calling tools like addcontacttocampaign if you only want read-only access.

Q: How do I handle large lead lists from the MCP Server?

Set includeresources=True when configuring the tool spec. This allows the framework to chunk and index massive JSON responses without blowing up your context window.

Turn outbound sales data into a semantic search engine. LlamaIndex indexes Adversus campaigns to query actual call center metrics.

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Connect Adversus MCP to LlamaIndex

Create your Vinkius account to connect Adversus 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 Adversus with LlamaIndex

Ask AI about this MCP

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Claude

Perplexity

Index Sales Campaigns

Querying past performance usually means exporting CSVs and building pivot tables. You can skip that by running `list_campaigns` and feeding the raw MCP tool output straight into your LlamaIndex vector store. The framework embeds the metadata for instant retrieval. When a manager asks which campaigns performed best last quarter, the RAG application searches that index. It cross-references the initial query with `get_campaign_details` to pull specific metrics. You get answers grounded in real API data, not hallucinations.

Build a Lead Knowledge Base

Sales teams lose track of who sits in which dialing queue. Calling `list_active_leads` pulls the global pipeline into your document store. Your application maps the relationships between leads and their assigned projects. Users can then ask the system natural language questions about prospect distribution. The agent runs `list_crm_projects` to verify current active buckets. It instantly returns a factual summary of where your potential buyers are currently parked.

Query the LlamaIndex MCP Server

Finding out who manages specific outbound efforts takes too long. Using `list_account_users` grabs the team directory and indexes their roles. The system knows exactly who owns what at any given moment. If someone needs to check a specific rep's workload, the agent pulls `list_campaign_contacts` for their assigned campaigns. The RAG pipeline synthesizes this information into a quick text response. You find out exactly how many calls remain in the queue without opening a dashboard.

Setup guide

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

Run `pip install llama-index-tools-mcp` first. Set up `BasicMCPClient` with your endpoint, wrap it in `McpToolSpec`, and call `to_tool_list_async()` to pass them to your FunctionAgent.

You can restrict access using the `allowed_tools` parameter. This prevents the agent from calling tools like `add_contact_to_campaign` if you only want read-only access.

Both. You can configure the agent to check the vector store first, then hit the live endpoint if the information is stale.

Set `include_resources=True` when configuring the tool spec. This allows the framework to chunk and index massive JSON responses without blowing up your context window.

Lead pipelines and dialing metrics pass through a zero-trust architecture. Vinkius provisions a temporary connection that vanishes when the query completes. Your vector store is the only place the information persists.

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