How to Use the folk MCP in LlamaIndex

Q: Can I search folk notes with LlamaIndex?

Yes. By indexing the output of listcontactnotes, you make your qualitative relationship data queryable via natural language.

Index your relationship data into LlamaIndex for semantic search and grounded responses across your entire contact list.

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Connect folk MCP to LlamaIndex

Create your Vinkius account to connect folk 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 folk with LlamaIndex

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Semantic indexing for folk contacts

Use `list_contacts` to pull your full database into a searchable LlamaIndex vector store. Your agent no longer guesses; it queries your actual relationship data. This turns your static contact list into a dynamic knowledge base. You can ask questions about your network and get answers grounded in hard data.

Ground responses in interaction history

Call `list_interactions` to populate your index with the history of your professional relationships. Your agent sees exactly what was said and when. Retrieve these insights via semantic search to build better context. It stops hallucinations by anchoring every answer in the specific notes you've already captured.

Structured retrieval with folk tools

Fetch specific records using `get_contact_details` when you need a deep dive on a single entity. The tool returns clean, structured data for your index. Use `list_contact_notes` to grab the qualitative data that usually hides in unstructured text. Your agent then maps these notes to your broader knowledge graph.

Setup guide

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

The server provides the data, and your LlamaIndex pipeline handles the vectorization. It maps your contacts into a searchable space for fast retrieval.

Yes. By indexing the output of `list_contact_notes`, you make your qualitative relationship data queryable via natural language.

The MCP server uses standard auth to keep your data private. Only the specific records you fetch are processed by your local LlamaIndex instance.

Your index will need a refresh to reflect the changes. You can trigger the server to re-fetch contacts and update your vector store accordingly.

It accesses your CRM records, including names, emails, and interaction logs. These are handled as structured JSON payloads and never cached outside your local runtime.

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