How to Use the Olostep MCP in LlamaIndex

Q: Can I filter which tools Olostep exposes to LlamaIndex?

Yes, you can use an allowedtools filter during client setup. This ensures your agents only use the specific scraping functions you want them to access.

Q: How do I check my remaining credits in LlamaIndex?

Call the getusage tool as part of your data pipeline. You can log the output or add a check to stop the indexing process if your balance runs low.

Index live web data in LlamaIndex. Turn Olostep scraping results into searchable knowledge for your RAG applications.

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Connect Olostep MCP to LlamaIndex

Create your Vinkius account to connect Olostep to LlamaIndex — we handle the hosting, security, and runtime updates so you don't have to. No server setup required.

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Setup Olostep with LlamaIndex

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Ingest web content into LlamaIndex

Scrape live pages and convert them into searchable documents. Use `scrape_url` to grab fresh content, then pass that data directly into your vector store for retrieval-augmented generation. This keeps your index grounded in real-time data instead of stale documents. Your agents can answer questions based on the latest site changes fetched via the server tools.

Perform bulk indexing with batch tools

Scale your knowledge base by scraping entire domains at once. Send your list of target URLs to `create_batch` and let the system handle the heavy lifting of rendering and extraction. Once the job finishes, use `get_batch_results` to collect the structured output. This method makes it easy to process thousands of pages and update your index in one go.

Organize agents with scraping capabilities

Use `list_agents` and `get_agent` to manage your specific scraping configurations. You can set up different agents for different parts of your site structure to ensure high-quality data extraction. Each agent acts as a specialized tool within your LlamaIndex workflow. This modularity allows you to tailor your scraping parameters to the specific needs of your RAG pipeline.

Setup guide

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

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Common questions about Olostep MCP in LlamaIndex

Use the MCP tool spec adapter to convert the server tools into LlamaIndex functions. This allows your agents to call them as native tools during the indexing process.

By feeding fresh, accurate data from the web into your index, you provide the context needed for grounded answers. This keeps your model focused on the facts retrieved from the target sites.

Yes, you can use an allowed_tools filter during client setup. This ensures your agents only use the specific scraping functions you want them to access.

Call the `get_usage` tool as part of your data pipeline. You can log the output or add a check to stop the indexing process if your balance runs low.

Your scraping configurations and the resulting content are isolated to your specific instance. Olostep processes the page data, and LlamaIndex stores it in your private local vector store, keeping everything secure.

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