ParseHub MCP Server for OpenAI Agents SDK 10 tools — connect in under 2 minutes
The OpenAI Agents SDK enables production-grade agent workflows in Python. Connect ParseHub through Vinkius and your agents gain typed, auto-discovered tools with built-in guardrails. no manual schema definitions required.
ASK AI ABOUT THIS MCP SERVER
Vinkius supports streamable HTTP and SSE.
import asyncio
from agents import Agent, Runner
from agents.mcp import MCPServerStreamableHttp
async def main():
# Your Vinkius token. get it at cloud.vinkius.com
async with MCPServerStreamableHttp(
url="https://edge.vinkius.com/[YOUR_TOKEN_HERE]/mcp"
) as mcp_server:
agent = Agent(
name="ParseHub Assistant",
instructions=(
"You help users interact with ParseHub. "
"You have access to 10 tools."
),
mcp_servers=[mcp_server],
)
result = await Runner.run(
agent, "List all available tools from ParseHub"
)
print(result.final_output)
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 ParseHub MCP Server
Bring ParseHub Cloud Scraping directly into your AI workflows. Manage pre-configured web scraping targets natively and orchestrate complex headless browser automation directly from chat. Dispatch run jobs on command, query execution status limits, and extract final parsed payloads securely.
The OpenAI Agents SDK auto-discovers all 10 tools from ParseHub through native MCP integration. Build agents with built-in guardrails, tracing, and handoff patterns. chain multiple agents where one queries ParseHub, another analyzes results, and a third generates reports, all orchestrated through Vinkius.
What you can do
- Project Navigation — Inspect and list configured ParseHub projects, determining start URLs, templates, and total crawler pages attached
- Execution Dispatch — Command remote servers to trigger specific headless data extraction jobs
run_projectoptionally overriding starting URLs natively - Observability Tracing — Monitor exactly where a
Runobject is (queued, initialized, running, complete) without checking the desktop app - Payload Extraction — Pull down structured arrays containing the scraped payloads securely via
get_run_datamatching explicit datasets
The ParseHub MCP Server exposes 10 tools through the Vinkius. Connect it to OpenAI Agents SDK 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 ParseHub to OpenAI Agents SDK via MCP
Follow these steps to integrate the ParseHub MCP Server with OpenAI Agents SDK.
Install the SDK
Run pip install openai-agents in your Python environment
Replace the token
Replace [YOUR_TOKEN_HERE] with your Vinkius token from cloud.vinkius.com
Run the script
Save the code above and run it: python agent.py
Explore tools
The agent will automatically discover 10 tools from ParseHub
Why Use OpenAI Agents SDK with the ParseHub MCP Server
OpenAI Agents SDK provides unique advantages when paired with ParseHub through the Model Context Protocol.
Native MCP integration via `MCPServerSse`, pass the URL and the SDK auto-discovers all tools with full type safety
Built-in guardrails, tracing, and handoff patterns let you build production-grade agents without reinventing safety infrastructure
Lightweight and composable: chain multiple agents and MCP servers in a single pipeline with minimal boilerplate
First-party OpenAI support ensures optimal compatibility with GPT models for tool calling and structured output
ParseHub + OpenAI Agents SDK Use Cases
Practical scenarios where OpenAI Agents SDK combined with the ParseHub MCP Server delivers measurable value.
Automated workflows: build agents that query ParseHub, process the data, and trigger follow-up actions autonomously
Multi-agent orchestration: create specialist agents. one queries ParseHub, another analyzes results, a third generates reports
Data enrichment pipelines: stream data through ParseHub tools and transform it with OpenAI models in a single async loop
Customer support bots: agents query ParseHub to resolve tickets, look up records, and update statuses without human intervention
ParseHub MCP Tools for OpenAI Agents SDK (10)
These 10 tools become available when you connect ParseHub to OpenAI Agents SDK via MCP:
cancel_run
If the run was already scraping pages, partial data may be available. Data from already-scraped pages is preserved and can be retrieved with get_run_data. Use this to stop long-running scrapes or free up queue slots. Cancel a queued or actively running ParseHub run
delete_run
Cannot be undone. Use this to clean up old runs and free up storage quota on your account. Permanently delete a ParseHub run and its extracted data
get_last_ready_data
Ideal for dashboards or integrations that always want the freshest available data without managing individual run tokens. Instantly get the latest completed data for a ParseHub project
get_project
The project_token can be found via list_projects or in the ParseHub desktop client settings tab. Get detailed configuration of a specific ParseHub project
get_run_data
Only works when the run status is "complete" and data_ready is true. The JSON structure mirrors the template selection configuration set up in the ParseHub desktop client. Download the raw JSON data extracted from a completed ParseHub run
get_run_details
Poll this endpoint to wait for a run to complete before fetching data. Check the status of a specific ParseHub run
list_projects
Each project includes a project_token (unique identifier), title, last_run timestamp, and template configuration. Use the project_token for all subsequent run management operations. List all ParseHub web scraping projects
list_runs
Useful for auditing or finding a specific completed run to fetch data from. Get the history of all runs for a ParseHub project
run_project
Returns a run_token for tracking progress. The run enters a queue and begins processing within seconds. Use get_run to monitor and get_run_data to retrieve results once complete. Start a new ParseHub scraping run for a project
run_project_with_url
Perfect for scraping different pages with the same template (e.g., different product categories). The template extraction rules still apply unchanged — only the starting page changes. Start a ParseHub run targeting a custom URL instead of the project default
Example Prompts for ParseHub in OpenAI Agents SDK
Ready-to-use prompts you can give your OpenAI Agents SDK agent to start working with ParseHub immediately.
"Fetch the list of scrape projects I have on my ParseHub account."
"Start a new run for project 't9zx...' and check its status."
"Extract the finished data JSON payload from run ID 'run_k1l'."
Troubleshooting ParseHub MCP Server with OpenAI Agents SDK
Common issues when connecting ParseHub to OpenAI Agents SDK through the Vinkius, and how to resolve them.
MCPServerStreamableHttp not found
pip install --upgrade openai-agentsAgent not calling tools
ParseHub + OpenAI Agents SDK FAQ
Common questions about integrating ParseHub MCP Server with OpenAI Agents SDK.
How does the OpenAI Agents SDK connect to MCP?
MCPServerSse(url=...) to create a server connection. The SDK auto-discovers all tools and makes them available to your agent with full type information.Can I use multiple MCP servers in one agent?
MCPServerSse instances to the agent constructor. The agent can use tools from all connected servers within a single run.Does the SDK support streaming responses?
Connect ParseHub 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 ParseHub to OpenAI Agents SDK
Get your token, paste the configuration, and start using 10 tools in under 2 minutes. No API key management needed.