How to Use the Exa MCP in LlamaIndex

Index clean, conceptually relevant web data directly into LlamaIndex vector stores using Exa.

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Connect Exa MCP to LlamaIndex

Create your Vinkius account to connect Exa 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 Exa with LlamaIndex

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Feed clean web text into your LlamaIndex RAG pipeline

The `exa_get_contents` tool retrieves stripped, structured text from target web pages so you can index it without parsing messy HTML. This prevents garbage text from polluting your vector databases. By using this MCP Server, your LlamaIndex RAG applications generate answers grounded in actual web data rather than model hallucinations. It handles the extraction on the fly so you can focus on building your index.

Use this MCP Server to search the web semantically

The `exa_search` tool uses neural search technology to find web pages based on conceptual meaning instead of keyword matches. Your LlamaIndex agent can query the web and immediately convert the results into searchable document nodes. You can filter search results by category or date before indexing them. You get high-quality content, highlights, and relevance scores to keep your index clean and focused.

Discover related sources for your LlamaIndex agent

The `exa_find_similar` tool analyzes a target URL and returns semantically related pages to expand your knowledge base. When your RAG agent needs deeper context, it uses this tool to discover alternative sources. You can feed these new URLs back into your pipeline to build a broader, more complete index. It makes mapping out a technical topic fast and fully automated.

Setup guide

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

Initialize the basic client with your Vinkius endpoint and convert it to a tool list. Pass the tools to your function agent to start querying.

Yes. You can map the output of the search tool directly to LlamaIndex document nodes and insert them into your vector store.

It filters out noisy HTML elements like ads and navigation menus. Your model gets clean, relevant text chunks, which reduces retrieval errors and keeps costs down.

Yes, you can pass date constraints to the search tool to ensure your knowledge base only indexes recent information.

All search queries and target URLs are processed in an ephemeral V8 sandbox on Vinkius. We do not persist your search strings or the extracted web contents, ensuring your indexing pipeline remains private.

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