Octoparse MCP Server for LlamaIndex 10 tools — connect in under 2 minutes
LlamaIndex specializes in data-aware AI agents that connect LLMs to structured and unstructured sources. Add Octoparse as an MCP tool provider through Vinkius and your agents can query, analyze, and act on live data alongside your existing indexes.
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Vinkius supports streamable HTTP and SSE.
import asyncio
from llama_index.tools.mcp import BasicMCPClient, McpToolSpec
from llama_index.core.agent.workflow import FunctionAgent
from llama_index.llms.openai import OpenAI
async def main():
# Your Vinkius token. get it at cloud.vinkius.com
mcp_client = BasicMCPClient("https://edge.vinkius.com/[YOUR_TOKEN_HERE]/mcp")
mcp_tool_spec = McpToolSpec(client=mcp_client)
tools = await mcp_tool_spec.to_tool_list_async()
agent = FunctionAgent(
tools=tools,
llm=OpenAI(model="gpt-4o"),
system_prompt=(
"You are an assistant with access to Octoparse. "
"You have 10 tools available."
),
)
response = await agent.run(
"What tools are available in Octoparse?"
)
print(response)
asyncio.run(main())
* Every MCP server runs on Vinkius-managed infrastructure inside AWS - a purpose-built runtime with per-request V8 isolates, Ed25519 signed audit chains, and sub-40ms cold starts optimized for native MCP execution. See our infrastructure
About Octoparse MCP Server
Connect your Octoparse framework to your AI agent and turn cloud-based web scraping into a fully conversational command center.
LlamaIndex agents combine Octoparse tool responses with indexed documents for comprehensive, grounded answers. Connect 10 tools through Vinkius and query live data alongside vector stores and SQL databases in a single turn. ideal for hybrid search, data enrichment, and analytical workflows.
What you can do
- Task Execution — Trigger the launch engine using
start_taskwhenever data refresh is needed, or invokestop_taskto halt runaway crawlers instantly. - Status Monitoring — Keep a pulse on active bots by calling
get_task_status, or systematically drill down through your project taxonomy vialist_task_groupsandlist_tasks. - Data Ingestion — Dump the latest extracted web rows natively into the AI's context using
get_task_data, allowing the LLM to format, structure, or summarize the results immediately. - Token Operations — Authenticate dynamically utilizing
get_tokenwith your core credentials.
The Octoparse MCP Server exposes 10 tools through the Vinkius. Connect it to LlamaIndex in under two minutes — no API keys to rotate, no infrastructure to provision, no vendor lock-in. Your configuration, your data, your control.
How to Connect Octoparse to LlamaIndex via MCP
Follow these steps to integrate the Octoparse MCP Server with LlamaIndex.
Install dependencies
Run pip install llama-index-tools-mcp llama-index-llms-openai
Replace the token
Replace [YOUR_TOKEN_HERE] with your Vinkius token
Run the agent
Save to agent.py and run: python agent.py
Explore tools
The agent discovers 10 tools from Octoparse
Why Use LlamaIndex with the Octoparse MCP Server
LlamaIndex provides unique advantages when paired with Octoparse through the Model Context Protocol.
Data-first architecture: LlamaIndex agents combine Octoparse tool responses with indexed documents for comprehensive, grounded answers
Query pipeline framework lets you chain Octoparse tool calls with transformations, filters, and re-rankers in a typed pipeline
Multi-source reasoning: agents can query Octoparse, a vector store, and a SQL database in a single turn and synthesize results
Observability integrations show exactly what Octoparse tools were called, what data was returned, and how it influenced the final answer
Octoparse + LlamaIndex Use Cases
Practical scenarios where LlamaIndex combined with the Octoparse MCP Server delivers measurable value.
Hybrid search: combine Octoparse real-time data with embedded document indexes for answers that are both current and comprehensive
Data enrichment: query Octoparse to augment indexed data with live information before generating user-facing responses
Knowledge base agents: build agents that maintain and update knowledge bases by periodically querying Octoparse for fresh data
Analytical workflows: chain Octoparse queries with LlamaIndex's data connectors to build multi-source analytical reports
Octoparse MCP Tools for LlamaIndex (10)
These 10 tools become available when you connect Octoparse to LlamaIndex via MCP:
clear_task_data
Done to purge testing footprints before production crawls. Delete all securely stored data for an Octoparse task
get_task_data
Use offset-based pagination strictly to prevent memory crash exceptions (max 1000 limit). Export un-exported data from a completed Octoparse scraping task
get_task_status
Get the current running status of an Octoparse cloud task
get_token
0 password grant. Returns an access_token. The access_token must be stored and reused for API calls until expiration. Obtain an OAuth 2.0 access token from Octoparse
list_task_groups
Use these IDs to filter executing scraping tasks nested inside a specific folder limit. List all task groups (folders) in the Octoparse account
list_tasks
Each task includes a taskId, status, and creation date. Use the taskId for starting or polling data. List all configured cloud scraping tasks on Octoparse
mark_data_exported
Execute this immediately after a successful `get_task_data`. Mark all currently stored data in an Octoparse task as extracted
start_task
Task changes status to Running instantly. Start a cloud scraping task on Octoparse
stop_task
Stop a running Octoparse cloud task
update_task_params
g. changing the core search URL or injected keywords) without opening the Octoparse IDE cleanly scaling parameterized bots. Dynamically update URL or parameter constraints driving a task
Example Prompts for Octoparse in LlamaIndex
Ready-to-use prompts you can give your LlamaIndex agent to start working with Octoparse immediately.
"Look up task 'LinkedIn Profiles Q4' and tell me how many rows it extracted."
"Start my Amazon Price Monitor crawler task now."
"Get the data extracted from task 'Real Estate NYC' and format it as a markdown table."
Troubleshooting Octoparse MCP Server with LlamaIndex
Common issues when connecting Octoparse to LlamaIndex through the Vinkius, and how to resolve them.
BasicMCPClient not found
pip install llama-index-tools-mcpOctoparse + LlamaIndex FAQ
Common questions about integrating Octoparse MCP Server with LlamaIndex.
How does LlamaIndex connect to MCP servers?
Can I combine MCP tools with vector stores?
Does LlamaIndex support async MCP calls?
Connect Octoparse with your favorite client
Step-by-step setup guides for every MCP-compatible client and framework:
Anthropic's native desktop app for Claude with built-in MCP support.
AI-first code editor with integrated LLM-powered coding assistance.
GitHub Copilot in VS Code with Agent mode and MCP support.
Purpose-built IDE for agentic AI coding workflows.
Autonomous AI coding agent that runs inside VS Code.
Anthropic's agentic CLI for terminal-first development.
Python SDK for building production-grade OpenAI agent workflows.
Google's framework for building production AI agents.
Type-safe agent development for Python with first-class MCP support.
TypeScript toolkit for building AI-powered web applications.
TypeScript-native agent framework for modern web stacks.
Python framework for orchestrating collaborative AI agent crews.
Leading Python framework for composable LLM applications.
Data-aware AI agent framework for structured and unstructured sources.
Microsoft's framework for multi-agent collaborative conversations.
Connect Octoparse to LlamaIndex
Get your token, paste the configuration, and start using 10 tools in under 2 minutes. No API key management needed.