How to Use the X (Twitter) MCP in LlamaIndex

Index X (Twitter) results into searchable knowledge graphs with LlamaIndex.

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Connect X (Twitter) MCP to LlamaIndex

Create your Vinkius account to connect X (Twitter) 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 X (Twitter) with LlamaIndex

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Indexing user profiles via `lookup_user_by_username`

When you call `lookup_user_by_username`, the tool retrieves a user's full profile data, including their bio and follower count. With LlamaIndex, this output isn't just displayed—it becomes part of your searchable knowledge base. This means you can query past sessions or configurations months later and get answers grounded in that actual API data, not some hallucination.

Knowledge-based search via `search_recent_tweets`

The `search_recent_tweets` tool lets you pull recent public tweets using specific keywords or hashtags. When LlamaIndex processes this, the results are indexed into a vector store. You can then build RAG applications that combine live API data with your own documents, making everything queryable together in one unified index.

Fact-checking specific tweets via `get_tweet_details`

Use `get_tweet_details` to pull the text and engagement metrics for a single numeric Tweet ID. This output gets chunked and stored as verifiable facts in your knowledge base. If you need to prove a claim, you query the index, and LlamaIndex grounds its answer directly on the specific tweet details data it indexed.

Setup guide

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

You use LlamaIndex to index recent searches. Instead of just seeing a list, you build an index that lets you query the *concept* behind those trends later on.

Yes. The whole point is indexing live API data. You can combine today's search results with last month's documents into one queryable index for deep analysis.

Because you get memory. Calling a tool gives an answer; indexing it means the answer becomes permanent, searchable knowledge that your AI client can reference later.

It supports multiple sources and resources because you pass tools to FunctionAgent. It's built for combining different data types into one queryable structure.

The server touches tweet text and user profiles. When indexing this knowledge base, make sure you are comfortable storing all that public API data in your vector store.

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