Octoparse MCP Server for Pydantic AI 10 tools — connect in under 2 minutes
Pydantic AI brings type-safe agent development to Python with first-class MCP support. Connect Octoparse through Vinkius and every tool is automatically validated against Pydantic schemas. catch errors at build time, not in production.
ASK AI ABOUT THIS MCP SERVER
Vinkius supports streamable HTTP and SSE.
import asyncio
from pydantic_ai import Agent
from pydantic_ai.mcp import MCPServerHTTP
async def main():
# Your Vinkius token. get it at cloud.vinkius.com
server = MCPServerHTTP(url="https://edge.vinkius.com/[YOUR_TOKEN_HERE]/mcp")
agent = Agent(
model="openai:gpt-4o",
mcp_servers=[server],
system_prompt=(
"You are an assistant with access to Octoparse "
"(10 tools)."
),
)
result = await agent.run(
"What tools are available in Octoparse?"
)
print(result.data)
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.
Pydantic AI validates every Octoparse tool response against typed schemas, catching data inconsistencies at build time. Connect 10 tools through Vinkius and switch between OpenAI, Anthropic, or Gemini without changing your integration code. full type safety, structured output guarantees, and dependency injection for testable agents.
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 Pydantic AI 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 Pydantic AI via MCP
Follow these steps to integrate the Octoparse MCP Server with Pydantic AI.
Install Pydantic AI
Run pip install pydantic-ai
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 with type-safe schemas
Why Use Pydantic AI with the Octoparse MCP Server
Pydantic AI provides unique advantages when paired with Octoparse through the Model Context Protocol.
Full type safety: every MCP tool response is validated against Pydantic models, catching data inconsistencies before they reach your application
Model-agnostic architecture. switch between OpenAI, Anthropic, or Gemini without changing your Octoparse integration code
Structured output guarantee: Pydantic AI ensures tool results conform to defined schemas, eliminating runtime type errors
Dependency injection system cleanly separates your Octoparse connection logic from agent behavior for testable, maintainable code
Octoparse + Pydantic AI Use Cases
Practical scenarios where Pydantic AI combined with the Octoparse MCP Server delivers measurable value.
Type-safe data pipelines: query Octoparse with guaranteed response schemas, feeding validated data into downstream processing
API orchestration: chain multiple Octoparse tool calls with Pydantic validation at each step to ensure data integrity end-to-end
Production monitoring: build validated alert agents that query Octoparse and output structured, schema-compliant notifications
Testing and QA: use Pydantic AI's dependency injection to mock Octoparse responses and write comprehensive agent tests
Octoparse MCP Tools for Pydantic AI (10)
These 10 tools become available when you connect Octoparse to Pydantic AI 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 Pydantic AI
Ready-to-use prompts you can give your Pydantic AI 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 Pydantic AI
Common issues when connecting Octoparse to Pydantic AI through the Vinkius, and how to resolve them.
MCPServerHTTP not found
pip install --upgrade pydantic-aiOctoparse + Pydantic AI FAQ
Common questions about integrating Octoparse MCP Server with Pydantic AI.
How does Pydantic AI discover MCP tools?
MCPServerHTTP instance with the server URL. Pydantic AI connects, discovers all tools, and generates typed Python interfaces automatically.Does Pydantic AI validate MCP tool responses?
Can I switch LLM providers without changing MCP code?
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 Pydantic AI
Get your token, paste the configuration, and start using 10 tools in under 2 minutes. No API key management needed.