How to Use the Influencers Club MCP in LlamaIndex

Q: Can I build a RAG application using Influencers Club and LlamaIndex?

Yes, you can use searchcreators to fetch profiles and index them into a VectorStoreIndex. This lets your LlamaIndex agent query your local creator database using natural language.

Q: Can I cache Influencers Club query results in LlamaIndex?

Yes, you can store the outputs of getaudienceoverlap in a local key-value store or vector index. This allows your LlamaIndex pipeline to retrieve audience metrics instantly without making redundant API calls.

Q: How do I restrict which Influencers Club MCP tools LlamaIndex can access?

You pass an allowedtools list to the MCP tool spec constructor during initialization. This lets you restrict your agent to only use getuserinfo or getusage while blocking write operations.

Q: Is the creator email data safe when using Influencers Club with LlamaIndex?

Yes, all email addresses passed to enrichbyemail are sent through encrypted TLS connections inside the Vinkius sandbox. No personal email data is stored on the intermediate server, maintaining strict compliance with privacy standards.

Index raw creator metrics from the Influencers Club MCP Server directly into LlamaIndex vector stores.

See Vinkius in Action

Works with every AI agent you already use

…and any MCP-compatible client

MCP Servers - Free for Subscribers

Connect Influencers Club MCP to LlamaIndex

Create your Vinkius account to connect Influencers Club 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.

GDPR Free for Subscribers

Setup Influencers Club with LlamaIndex

Ask AI about this MCP

ChatGPT

Claude

Perplexity

Index Creator Profiles in LlamaIndex

The `enrich_by_handle` tool extracts deep demographic data from social handles and feeds it directly into your LlamaIndex document pipeline. This process converts raw JSON payloads into structured index nodes, making creator profiles fully searchable via semantic queries. You write natural language queries to find creators who match specific styles or niches. LlamaIndex searches the local vector store of enriched profiles, bypassing repetitive API calls by retrieving cached, structured data.

Build RAG Pipelines with Historical Creator Posts

The `get_creator_posts` tool retrieves historical content from target creators to populate your LlamaIndex knowledge base. Your pipeline indexes these posts, allowing your agent to analyze writing styles, posting frequency, and past brand mentions. When you prompt the agent to draft a sponsorship pitch, it queries this index to reference specific past posts. This grounds the generated pitch in actual historical data, preventing hallucinations and ensuring the message resonates with the creator's real voice.

Filter Lookalikes via Semantically Indexed Overlaps

The `get_similar_creators` tool fetches lookalike profiles and stores them as connected nodes inside a LlamaIndex property graph. Your agent uses these nodes to map relationships between different creator niches. By combining this graph with `get_audience_overlap` data, the system evaluates how closely related two creator communities are. You query the graph to find clusters of low-overlap, high-similarity creators, identifying fresh audiences for your campaigns.

Setup guide

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

Get your connection token →

Independent Platform Disclaimer: Vinkius is an independent platform and is not affiliated with, endorsed by, sponsored by, verified by, or otherwise authorized by Influencers Club. 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.

Why Choose Vinkius

Vinkius connects your tools to AI with real-time monitoring and automatic cost savings — all from one dashboard.

Connect Influencers Club now

Real-time monitoring

Live

visibility into every interaction

Connect your favorite tools to your AI and see exactly what's happening — every request, every response, in real time.

Built-in savings

60%

lower AI costs

Vinkius compresses data between your apps and your AI automatically. Lower bills every month — no configuration required.

Single dashboard

One

place for every integration

Every tool your AI connects to, managed from a single screen. One account, complete control.

Common questions about Influencers Club MCP in LlamaIndex

Yes, you can use `search_creators` to fetch profiles and index them into a VectorStoreIndex. This lets your LlamaIndex agent query your local creator database using natural language.

LlamaIndex wraps the MCP tools into McpToolSpec objects. Tools like `enrich_by_email` output structured JSON that is converted into Document objects for indexing.

Yes, you can store the outputs of `get_audience_overlap` in a local key-value store or vector index. This allows your LlamaIndex pipeline to retrieve audience metrics instantly without making redundant API calls.

You pass an allowed_tools list to the MCP tool spec constructor during initialization. This lets you restrict your agent to only use `get_user_info` or `get_usage` while blocking write operations.

Yes, all email addresses passed to `enrich_by_email` are sent through encrypted TLS connections inside the Vinkius sandbox. No personal email data is stored on the intermediate server, maintaining strict compliance with privacy standards.

Use it with your favorite AI tools

Connect this server to Cursor, Claude, VS Code, and more.

OpenAI Agents SDK sdk-python

Google ADK sdk-python

Pydantic AI sdk-python

Vercel AI SDK sdk-typescript