How to Use the Persana AI MCP in LlamaIndex

Q: How do I index Persana AI tool outputs into a LlamaIndex vector store?

You run enrichcompany or enrichperson via the MCP tool spec and pass the resulting text blocks to your index ingestion pipeline. LlamaIndex converts this profile data into vector nodes, making it searchable for your personalized email generation steps.

Q: Can LlamaIndex query historical prospect lists managed by Persana AI?

Yes, your query engine can call getleadlist to fetch specific list details and index them as a document source. This allows your agent to answer complex questions about your current pipeline using actual, live list data.

Index live prospect data and intent signals directly into your LlamaIndex RAG pipelines for hyper-personalized outreach.

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Connect Persana AI MCP to LlamaIndex

Create your Vinkius account to connect Persana AI 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 Persana AI with LlamaIndex

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Turn live prospect data into queryable LlamaIndex nodes

The `enrich_person` tool retrieves deep background profiles that your LlamaIndex pipeline indexes into vector memory for semantic search. Instead of querying a static database, your RAG application searches through freshly enriched contact profiles to find relevant hooks for your sales emails. This prevents your LLM from hallucinating prospect details during generation. By combining live data with historical account context, your LlamaIndex agent writes highly accurate pitches grounded in real professional histories.

Track career transitions to update your vector index

The `find_job_changes` tool scans your target accounts for recent role shifts and feeds these updates directly to your LlamaIndex knowledge base. When a key decision-maker moves, your system indexes the event as a high-priority intent node. Your query engine can then retrieve these transition events to trigger automated congratulations sequences. This ensures your sales context stays fresh without requiring manual database updates.

Build a dynamic MCP Server search engine for B2B intelligence

The `search_people` tool allows your LlamaIndex agent to query the live market and feed the results directly into your document indexes. Your agent runs structured searches, processes the raw profiles, and stores them as structured nodes for downstream retrieval. Because this MCP Server integrates with the LlamaIndex MCP tool spec, you can restrict which tools the agent has access to using allowed filters. This keeps your agent focused on prospecting tasks while controlling your API consumption.

Setup guide

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

You run `enrich_company` or `enrich_person` via the MCP tool spec and pass the resulting text blocks to your index ingestion pipeline. LlamaIndex converts this profile data into vector nodes, making it searchable for your personalized email generation steps.

Yes, your query engine can call `get_lead_list` to fetch specific list details and index them as a document source. This allows your agent to answer complex questions about your current pipeline using actual, live list data.

The LlamaIndex FunctionAgent evaluates your query and calls `search_people` when it needs fresh contact data that isn't in your local index. This dynamic tool-calling ensures your RAG pipeline always operates with real-time market data.

Yes, you can specify exactly which tools to expose, such as limiting the agent to `verify_email` and `lookup_email` for validation tasks. This prevents the agent from making unnecessary or expensive enrichment calls during basic search workflows.

Your credentials and list data are managed entirely through the Vinkius MCP control plane, never exposing your keys to the LLM. All data pulled via `list_lead_lists` is processed in memory within ephemeral V8 isolates, preventing exposure of sensitive prospect information.

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